Search Results for author:
Found 33 papers, 10 papers with code
VISION2UI: A Real-World Dataset with Layout for Code Generation from UI Designs
Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams.
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging.
A Novel Hybrid Ordinal Learning Model with Health Care Application
This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout.
KGLens: A Parameterized Knowledge Graph Solution to Assess What an LLM Does and Doesn't Know
Measuring the alignment between a Knowledge Graph (KG) and Large Language Models (LLMs) is an effective method to assess the factualness and identify the knowledge blind spots of LLMs.
Ordinal Classification with Distance Regularization for Robust Brain Age Prediction
However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.
Ranked #1 on
Ordinal Classification
on OASIS+NACC+ICBM+ABIDE+IXI
Leveraging Large Language Models for Exploiting ASR Uncertainty
While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on off-the-shelf automatic speech recognition (ASR) systems for transcription, or be equipped with an in-built speech modality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts
Popular convolutional neural networks, which are designated for local context capturing, have never been applied to TPP modelling due to their incapability of modelling in continuous time.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Test-Time Adaptation with Perturbation Consistency Learning
Accordingly, in this paper, we propose perturbation consistency learning (PCL), a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts.
A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry
Objective: To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline.
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death.
Data-Driven Offline Decision-Making via Invariant Representation Learning
The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active interaction.
Improved Prediction of Beta-Amyloid and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding
Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics (SPHARM).
Greykite: Deploying Flexible Forecasting at Scale at LinkedIn
We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn.
Offline RL for Natural Language Generation with Implicit Language Q Learning
Large language models distill broad knowledge from text corpora.
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control.
Deep residual inception encoder-decoder network for amyloid PET harmonization
Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis.
Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy
We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Tianshou: a Highly Modularized Deep Reinforcement Learning Library
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.
Online Adaptation to Label Distribution Shift
Machine learning models often encounter distribution shifts when deployed in the real world.
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
Optimizing Rankings for Recommendation in Matching Markets
Based on the success of recommender systems in e-commerce, there is growing interest in their use in matching markets (e. g., labor).
Game-based Pricing and Task Offloading in Mobile Edge Computing Enabled Edge-Cloud Systems
In this paper, we formulate a distributed mechanism to analyze the interaction between OSPs and IoT MDs in the MEC enabled edge-cloud system by appling multi-leader multi-follower two-tier Stackelberg game theory.
Edge-computing
Computer Science and Game Theory
Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition
With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0. 05$ are 116, 279 and 387 for the longitudinal $A\beta+$ AD, $A\beta+$ mild cognitive impairment (MCI) and $A\beta+$ CU groups, respectively.
Off-policy Bandits with Deficient Support
Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e. g. voice assistants, recommendation, search), since it enables the reuse of large amounts of log data.
Accurate Tumor Tissue Region Detection with Accelerated Deep Convolutional Neural Networks
Our approach was approximately 100 times faster than the original DCNN approach while simultaneously preserving high accuracy and precision.
Adaptive Estimator Selection for Off-Policy Evaluation
We develop a generic data-driven method for estimator selection in off-policy policy evaluation settings.
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data.
Doubly robust off-policy evaluation with shrinkage
We propose a new framework for designing estimators for off-policy evaluation in contextual bandits.
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm.
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
In this study, a multi-task deep neural network is proposed for skin lesion analysis.
