Search Results for author:
Found 147 papers, 53 papers with code
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition
General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.
MCS: An In-battle Commentary System for MOBA Games
This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games.
MovieChats: Chat like Humans in a Closed Domain
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
Taming Stable Diffusion for Text to 360° Panorama Image Generation
Generative models, e. g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts.
FiP: a Fixed-Point Approach for Causal Generative Modeling
Based on this, we design a two-stage causal generative model that first infers the causal order from observations in a zero-shot manner, thus by-passing the search, and then learns the generative fixed-point SCM on the ordered variables.
Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem
By modeling the inverse design of PCSEL as a sequential decision-making problem, RL approaches can construct a satisfactory PCSEL structure from scratch.
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation
Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs. Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase.
Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation.
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
This paper presents a robust watermark embedding scheme, named RobWE, to protect the ownership of personalized models in PFL.
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space.
Ranked #1 on
Low-Light Image Enhancement
on VV
Low-light Image Deblurring and Enhancement
Low-Light Image Enhancement
The Essential Role of Causality in Foundation World Models for Embodied AI
This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these.
LQER: Low-Rank Quantization Error Reconstruction for LLMs
Post-training quantization of Large Language Models (LLMs) is challenging.
Multi-modal News Understanding with Professionally Labelled Videos (ReutersViLNews)
Towards a solution for designing this ability in algorithms, we present a large-scale analysis on an in-house dataset collected by the Reuters News Agency, called Reuters Video-Language News (ReutersViLNews) dataset which focuses on high-level video-language understanding with an emphasis on long-form news.
TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion
In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation.
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
In this paper, we formulate PAR as a vision-language fusion problem and fully exploit the relations between pedestrian images and attribute labels.
Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue.
Learned Causal Method Prediction
In this work, we propose CAusal Method Predictor (CAMP), a framework for predicting the best method for a given dataset.
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner.
ARTree: A Deep Autoregressive Model for Phylogenetic Inference
Designing flexible probabilistic models over tree topologies is important for developing efficient phylogenetic inference methods.
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
In this work, we explore the statistical and learning properties of the LLM layer and attribute the bottleneck of LLM quantisation to numerical scaling offsets.
1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting
Given the superiority of capsule network (CapsNet) over CNN in various forecasting and classification tasks, this study investigates the potential of integrating a 1D CapsNet with an LSTM network for multi-step stock index forecasting.
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Foundation models have brought changes to the landscape of machine learning, demonstrating sparks of human-level intelligence across a diverse array of tasks.
ITI-GEN: Inclusive Text-to-Image Generation
We show that, for some attributes, images can represent concepts more expressively than text.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Semi-Implicit Variational Inference via Score Matching
Semi-implicit variational inference (SIVI) greatly enriches the expressiveness of variational families by considering implicit variational distributions defined in a hierarchical manner.
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation
To address this issue, we propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can effectively capture and utilize accurate degradation representation for image restoration.
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.
One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms
In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP.
OVO: Open-Vocabulary Occupancy
Semantic occupancy prediction aims to infer dense geometry and semantics of surroundings for an autonomous agent to operate safely in the 3D environment.
Neural-PBIR Reconstruction of Shape, Material, and Illumination
In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination.
Ranked #1 on
Depth Prediction
on Stanford-ORB
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022
Accurately predicting the prices of financial time series is essential and challenging for the financial sector.
Understanding Causality with Large Language Models: Feasibility and Opportunities
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.
Automatic Generation of Multiple-Choice Questions
Moreover, we present a novel approach to automatically generate adequate distractors for a given QAP.
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data.
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles.
Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks
Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference.
Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis
ChatGPT has demonstrated exceptional proficiency in natural language conversation, e. g., it can answer a wide range of questions while no previous large language models can.
TokenHPE: Learning Orientation Tokens for Efficient Head Pose Estimation via Transformers
To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned.
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification
To this end, two novel modules are proposed to leverage the characteristics of bird images, namely, the hierarchy stage feature aggregation (HSFA) module and the feature in feature abstraction (FFA) module.
Ranked #7 on
Fine-Grained Image Classification
on NABirds
WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT
Cochlear implantation is currently the most effective treatment for patients with severe deafness, but mastering cochlear implantation is extremely challenging because the temporal bone has extremely complex and small three-dimensional anatomical structures, and it is important to avoid damaging the corresponding structures when performing surgery.
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering
Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL.
Deformable Object Manipulation
Model-based Reinforcement Learning
+2
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Given the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to consider non-linear relations between variables, instantaneous effects and history-dependent noise (the change of noise distribution due to past actions).
Tag-Set-Sequence Learning for Generating Question-Answer Pairs
Transformer-based QG models can generate question-answer pairs (QAPs) with high qualities, but may also generate silly questions for certain texts.
Learn the Time to Learn: Replay Scheduling in Continual Learning
In such settings, we propose that continual learning systems should learn the time to learn and schedule which tasks to replay at different time steps.
Optimistic Optimization of Gaussian Process Samples
Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions.
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data.
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design.
Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines
We present a system called TP3 to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article.
A Variational Approach to Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms.
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes
In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP).
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
In computer-aided design (CAD) community, the point cloud data is pervasively applied in reverse engineering, where the point cloud analysis plays an important role.
Local Constraint-Based Causal Discovery under Selection Bias
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present.
Learning with Free Object Segments for Long-Tailed Instance Segmentation
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects.
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations
In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks.
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace
In this study, a real-time dispatching algorithm based on reinforcement learning is proposed and for the first time, is deployed in large scale.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Optimal transport for causal discovery
With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-nonlinear models.
Identifiable Generative Models for Missing Not at Random Data Imputation
In this work, we fill in this gap by systematically analyzing the identifiability of generative models under MNAR.
Simultaneous Missing Value Imputation and Structure Learning with Groups
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
Causal Triple Attention Time Series Forecasting
Time series forecasting has historically been a key area of academic research and industrial applications.
FCause: Flow-based Causal Discovery
Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values.
Intervention Adversarial Auto-Encoder
In this paper we propose a new method to stabilize the training process of the latent variables of adversarial auto-encoders, which we name Intervention Adversarial auto-encoder (IVAAE).
Learn the Time to Learn: Replay Scheduling for Continual Learning
Inspired by human learning, we illustrate that scheduling over which tasks to revisit is critical to the final performance with finite memory resources.
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering
We found that many of the "unknowns" to the learned VQA model are indeed "known" in the dataset implicitly.
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed.
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization
Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods.
On Incorrectness Logic and Kleene Algebra with Top and Tests
In his seminal work, Kozen proved that KAT subsumes propositional Hoare logic, showing that one can reason about the (partial) correctness of while programs by means of the equational theory of KAT.
PVT: Point-Voxel Transformer for Point Cloud Learning
The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks.
Ranked #18 on
3D Part Segmentation
on ShapeNet-Part
Toward Integrated Human-machine Intelligence for Civil Engineering: An Interdisciplinary Perspective
This paper will lay the foundation for identifying relevant studies to form a research roadmap to address the four knowledge gaps identified.
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size.
Countering Adversarial Examples: Combining Input Transformation and Noisy Training
Prior work has shown that JPEG compression can combat the drop in classification accuracy on adversarial examples to some extent.
Defending Adversaries Using Unsupervised Feature Clustering VAE
We propose a modified VAE (variational autoencoder) as a denoiser to remove adversarial perturbations for image classification.
Probabilistic DAG Search
Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search -- most famously, the game of Go.
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Bayesian neural networks and deep ensembles represent two modern paradigms of uncertainty quantification in deep learning.
Causally Constrained Data Synthesis for Private Data Release
However for real-world applications, the privacy of data is critical.
Variational Bayesian Supertrees
Given overlapping subsets of a set of taxa (e. g. species), and posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we infer a posterior distribution on phylogenetic tree topologies for the entire taxon set?
Contextual HyperNetworks for Novel Feature Adaptation
While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challenge, as neural networks are commonly trained to produce a fixed output dimension.
Holistic 3D Scene Understanding from a Single Image with Implicit Representation
We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
Ranked #1 on
Monocular 3D Object Detection
on SUN RGB-D
(using extra training data)
Pointwise Weyl Laws for Schrödinger operators with singular potentials
We consider the Schr\"odinger operators $H_V=-\Delta_g+V$ with singular potentials $V$ on general $n$-dimensional Riemannian manifolds and study whether various forms of pointwise Weyl law remain valid under this pertubation.
Analysis of PDEs Mathematical Physics Classical Analysis and ODEs Functional Analysis Mathematical Physics Spectral Theory 58J50, 35P15
WLAN-Log-Based Superspreader Detection in the COVID-19 Pandemic
Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i. e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework.
Social and Information Networks Computers and Society
MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
Many objects do not appear frequently enough in complex scenes (e. g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e. g., in product images).
Estimating $α$-Rank by Maximizing Information Gain
We show the benefits of using information gain as compared to the confidence interval criterion of ResponseGraphUCB (Rowland et al. 2019), and provide theoretical results justifying our method.
Intervention Generative Adversarial Nets
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem.
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
By handling the non-Euclidean branch length space of phylogenetic models with carefully designed permutation equivariant transformations, VBPI-NF uses normalizing flows to provide a rich family of flexible branch length distributions that generalize across different tree topologies.
WUY at SemEval-2020 Task 7: Combining BERT and Naive Bayes-SVM for Humor Assessment in Edited News Headlines
For subtask B, we simply use a sequence-pair BERT model, the official accuracy of which is 0. 53196 and ranks 25th out of 32.
True-data Testbed for 5G/B5G Intelligent Network
Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting Internet of Everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service.
Reinforcement Learning with Efficient Active Feature Acquisition
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem.
A Study on Efficiency in Continual Learning Inspired by Human Learning
We study a setting where the pruning phase is given a time budget, and identify connections between iterative pruning and multiple sleep cycles in humans.
Extracting Body Text from Academic PDF Documents for Text Mining
Accurate extraction of body text from PDF-formatted academic documents is essential in text-mining applications for deeper semantic understandings.
Generating Adequate Distractors for Multiple-Choice Questions
This paper presents a novel approach to automatic generation of adequate distractors for a given question-answer pair (QAP) generated from a given article to form an adequate multiple-choice question (MCQ).
How Do Fair Decisions Fare in Long-term Qualification?
Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions.
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type.
Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management.
Meta Sequence Learning for Generating Adequate Question-Answer Pairs
Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) on the main points of the document.
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.
Intervention Generative Adversarial Networks
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem.
Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge
In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers.
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
DRIFT: Deep Reinforcement Learning for Functional Software Testing
Efficient software testing is essential for productive software development and reliable user experiences.
Meta-Learning Divergences of Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and broad applicability.
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets.
Dynamic gesture retrieval: searching videos by human pose sequence
The number of static human poses is limited, it is hard to retrieve the exact videos using one single pose as the clue.
Thoracic Disease Identification and Localization using Distance Learning and Region Verification
In this paper, we propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease.
Attention-based network for low-light image enhancement
Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.
A Causal View on Robustness of Neural Networks
We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measurement data.
AMRL: Aggregated Memory For Reinforcement Learning
In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on long-term memory in order to learn an optimal policy.
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight
In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students.
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.
Assessing the Memory Ability of Recurrent Neural Networks
To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence.
Adaptive Parameterization for Neural Dialogue Generation
For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
We discuss those differences and propose modifications to existing regularization techniques in order to better adapt them to RL.
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals
In this paper, we focused on improving VAEs for real-valued data that has heterogeneous marginal distributions.
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
In this paper, we revisit perturbation theory as a powerful way of improving the variational approximation.
Meta-Learning for Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and general applicability.
Generating an Overview Report over Many Documents
To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the ice-start problem, i. e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs.
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering
Specifically, we investigate the use of scene graphs derived from images for Visual QA: an image is abstractly represented by a graph with nodes corresponding to object entities and edges to object relationships.
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
We show that the data generated from our simulator have similar statistics as real-world data.
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
However, flexible tools such as artificial neural networks (ANNs) suffer from a lack of interpretability limiting their acceptability to clinicians.
Variational Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates.
A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
In this paper, we provide a new benchmark dataset for a challenging task in this application - classification of fruits, vegetables, and refrigerated products, e. g. milk packages and juice cartons, in grocery stores.
Continuous Word Embedding Fusion via Spectral Decomposition
In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings.
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment.
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes.
Dressing the boundary: on soliton solutions of the nonlinear Schrödinger equation on the half-line
Based on the theory of integrable boundary conditions (BCs) developed by Sklyanin, we provide a direct method for computing soliton solutions of the focusing nonlinear Schr\"odinger (NLS) equation on the half-line.
Exactly Solvable and Integrable Systems High Energy Physics - Theory Mathematical Physics Mathematical Physics
Learning Discriminative 3D Shape Representations by View Discerning Networks
In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.
Causal Discovery in the Presence of Missing Data
When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.
Non-bifurcating phylogenetic tree inference via the adaptive LASSO
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system.
Generalizing Tree Probability Estimation via Bayesian Networks
Probability estimation is one of the fundamental tasks in statistics and machine learning.
Applications
Active Mini-Batch Sampling using Repulsive Point Processes
We first show that the phenomenon of variance reduction by diversified sampling generalizes in particular to non-stationary point processes.
Causality Refined Diagnostic Prediction
In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction.
Advances in Variational Inference
Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models.
Perturbative Black Box Variational Inference
Black box variational inference (BBVI) with reparameterization gradients triggered the exploration of divergence measures other than the Kullback-Leibler (KL) divergence, such as alpha divergences.
FontCode: Embedding Information in Text Documents using Glyph Perturbation
We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document.
Determinantal Point Processes for Mini-Batch Diversification
The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data.
Probabilistic Path Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is an efficient and effective means of sampling posterior distributions on Euclidean space, which has been extended to manifolds with boundary.
Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation
Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy, such as after an Achilles Tendon Rupture (ATR).
Diagnostic Prediction Using Discomfort Drawings
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings.
Viewpoint and Topic Modeling of Current Events
There are multiple sides to every story, and while statistical topic models have been highly successful at topically summarizing the stories in corpora of text documents, they do not explicitly address the issue of learning the different sides, the viewpoints, expressed in the documents.
Diagnostic Prediction Using Discomfort Drawings with IBTM
The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.
Inter-Battery Topic Representation Learning
The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data.
Variational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC).
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process.
Factorized Topic Models
In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data.
