Search Results for author:
Found 147 papers, 33 papers with code
DeepCoDA: personalized interpretability for compositional health
Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust.
Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory
We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data.
Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Experimental (design) optimization is a key driver in designing and discovering new products and processes.
Revisiting the Dataset Bias Problem from a Statistical Perspective
To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.
Root Cause Explanation of Outliers under Noisy Mechanisms
We then represent the functional form of a target outlier leaf as a function of the node and edge noises.
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection
Recent data-privacy laws have sparked interest in machine unlearning, which involves removing the effect of specific training samples from a learnt model as if they were never present in the original training dataset.
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying
To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions.
Beyond Surprise: Improving Exploration Through Surprise Novelty
We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations.
Predictive Modeling through Hyper-Bayesian Optimization
Specifically, we propose a novel way of integrating model selection and BO for the single goal of reaching the function optima faster.
Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction
To bridge that gap, this work proposes to model two concurrent mechanisms that jointly control human motion: the Persistent process that runs continually on the global scale, and the Transient sub-processes that operate intermittently on the local context of the human while interacting with objects.
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions.
Zero-shot Sim2Real Adaptation Across Environments
However, simulators are generally incapable of accurately replicating real-world dynamics, and thus bridging the sim2real gap is an important problem in simulation based learning.
Gradient Descent in Neural Networks as Sequential Learning in RKBS
The study of Neural Tangent Kernels (NTKs) has provided much needed insight into convergence and generalization properties of neural networks in the over-parametrized (wide) limit by approximating the network using a first-order Taylor expansion with respect to its weights in the neighborhood of their initialization values.
Memory-Augmented Theory of Mind Network
Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cognitive structure.
Multi-weather Image Restoration via Domain Translation
Therefore, effective restoration of multi-weather degraded images is an essential prerequisite for successful functioning of such systems.
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
To the best of our knowledge, these are the first $\tilde{\mathcal{O}}(\frac{1}{K})$ bound and absolute zero sub-optimality bound respectively for offline RL with linear function approximation from adaptive data with partial coverage.
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator.
Black-box Few-shot Knowledge Distillation
Traditional KD methods require lots of labeled training samples and a white-box teacher (parameters are accessible) to train a good student.
Guiding Visual Question Answering with Attention Priors
This grounding guides the attention mechanism inside VQA models through a duality of mechanisms: pre-training attention weight calculation and directly guiding the weights at inference time on a case-by-case basis.
Fast Conditional Network Compression Using Bayesian HyperNetworks
We introduce a conditional compression problem and propose a fast framework for tackling it.
Persistent-Transient Duality in Human Behavior Modeling
We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions.
Learning to Constrain Policy Optimization with Virtual Trust Region
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update.
Learning Theory of Mind via Dynamic Traits Attribution
Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories.
Learning to Transfer Role Assignment Across Team Sizes
In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment.
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
In particular, whether in the noisy setting, the EI strategy with a standard incumbent converges is still an open question of the Gaussian process bandit optimization problem.
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious.
Uncertainty Aware System Identification with Universal Policies
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments.
Fast Model-based Policy Search for Universal Policy Networks
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning.
Episodic Policy Gradient Training
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.
Kernel Functional Optimisation
Traditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitations induced by the parametric forms.
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Moreover, we show that our method is more computationally efficient and has a better dependence on the effective dimension of the neural network than an online counterpart.
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard $Q-$learning.
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory.
Semantic Host-free Trojan Attack
In this paper, we propose a novel host-free Trojan attack with triggers that are fixed in the semantic space but not necessarily in the pixel space.
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research
This article is a field guide to transparent model design.
Expected Improvement-based Contextual Bandits
With a linear reward function, we demonstrate that our algorithm achieves a near-optimal regret.
Neural Latent Traversal with Semantic Constraints
Whilst Generative Adversarial Networks (GANs) generate visually appealing high resolution images, the latent representations (or codes) of these models do not allow controllable changes on the semantic attributes of the generated images.
Generative Pseudo-Inverse Memory
We propose Generative Pseudo-Inverse Memory (GPM), a class of deep generative memory models that are fast to write in and read out.
Reachability Traces for Curriculum Design in Reinforcement Learning
The objective in goal-based reinforcement learning is to learn a policy to reach a particular goal state within the environment.
Plug and Play, Model-Based Reinforcement Learning
This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene.
Clustering by Maximizing Mutual Information Across Views
We propose a novel framework for image clustering that incorporates joint representation learning and clustering.
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support
We propose a novel approach that uses a hybrid of offline learning with online exploration.
A New Representation of Successor Features for Transfer across Dissimilar Environments
Transfer in reinforcement learning is usually achieved through generalisation across tasks.
A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants.
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions.
Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation.
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms
Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended.
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks
To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond {the linearity regime} in the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.
Learning Asynchronous and Sparse Human-Object Interaction in Videos
Their interactions are sparse in time hence more faithful to the true underlying nature and more robust in inference and learning.
High Dimensional Level Set Estimation with Bayesian Neural Network
In particular, we consider two types of LSE problems: (1) \textit{explicit} LSE problem where the threshold level is a fixed user-specified value, and, (2) \textit{implicit} LSE problem where the threshold level is defined as a percentage of the (unknown) maximum of the objective function.
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
We propose two generic methods for improving semi-supervised learning (SSL).
Logically Consistent Loss for Visual Question Answering
We propose a new model-agnostic logic constraint to tackle this issue by formulating a logically consistent loss in the multi-task learning framework as well as a data organisation called family-batch and hybrid-batch.
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering
Video QA challenges modelers in multiple fronts.
Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs
For the first time a Neural Program is treated as a datum in memory, paving the ways for modular, recursive and procedural neural programming.
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning
In psychological game theory, guilt aversion necessitates modelling of agents that have theory about what other agents think, also known as Theory of Mind (ToM).
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the expected characteristics (length-scale, smoothness, cyclicity etc.)
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series.
Learning to Abstract and Predict Human Actions
We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction.
Distributional Reinforcement Learning via Moment Matching
We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return.
From deep to Shallow: Equivalent Forms of Deep Networks in Reproducing Kernel Krein Space and Indefinite Support Vector Machines
In this paper we explore a connection between deep networks and learning in reproducing kernel Krein space.
Bayesian Optimization with Missing Inputs
In real-world applications, BO often faces a major problem of missing values in inputs.
Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch.
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function.
DeepCoDA: personalized interpretability for compositional health data
We define personalized interpretability as a measure of sample-specific feature attribution, and view it as a minimum requirement for a precision health model to justify its conclusions.
Variational Hyper-Encoding Networks
Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta).
Dynamic Language Binding in Relational Visual Reasoning
We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering.
Incorporating Expert Prior in Bayesian Optimisation via Space Warping
In this paper, we represent the prior knowledge about the function optimum through a prior distribution.
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Again, it is unknown how to incorporate the expert prior knowledge about the global optimum into Bayesian optimization process.
Hierarchical Conditional Relation Networks for Video Question Answering
Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts.
Ranked #3 on
Audio-Visual Question Answering (AVQA)
on AVQA
Audio-Visual Question Answering (AVQA)
Question Answering
+4
Self-Attentive Associative Memory
Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions.
Ranked #1 on
Question Answering
on bAbi
Distributionally Robust Bayesian Quadrature Optimization
We adopt the distributionally robust optimization perspective to this problem by maximizing the expected objective under the most adversarial distribution.
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Optimising acquisition function in low dimensional subspaces allows our method to obtain accurate solutions within limited computational budget.
Bayesian Optimization with Unknown Search Space
Applying Bayesian optimization in problems wherein the search space is unknown is challenging.
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent.
Cost-aware Multi-objective Bayesian optimisation
We introduce a cost-aware multi-objective Bayesian optimisation with non-uniform evaluation cost over objective functions by defining cost-aware constraints over the search space.
Accelerating Experimental Design by Incorporating Experimenter Hunches
In this paper, we consider per-variable monotonic trend in the underlying property that results in a unimodal trend in those variables for a target value optimization.
Neural Reasoning, Fast and Slow, for Video Question Answering
While recent advances in lingual and visual question answering have enabled sophisticated representations and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process.
GraphDTA: prediction of drug–target binding affinity using graph convolutional networks
The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches.
Ranked #4 on
Drug Discovery
on KIBA
Sparse Spectrum Gaussian Process for Bayesian Optimization
Since the balance between predictive mean and the predictive variance is the key determinant to the success of Bayesian optimization, the current sparse spectrum methods are less suitable for it.
Neural Stored-program Memory
Neural networks powered with external memory simulate computer behaviors.
Ranked #5 on
Question Answering
on bAbi
(Mean Error Rate metric)
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors.
Ranked #6 on
Video Anomaly Detection
on HR-ShanghaiTech
Stable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions.
Multi-objective Bayesian optimisation with preferences over objectives
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B".
Improving Generalization and Stability of Generative Adversarial Networks
We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator.
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives
We initially tune the hyperparameters on a small subset of training data using Bayesian optimization.
Learning to Remember More with Less Memorization
Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning.
Ranked #5 on
Text Classification
on Yahoo! Answers
Graph Transformation Policy Network for Chemical Reaction Prediction
We address a fundamental problem in chemistry known as chemical reaction product prediction.
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
We address this problem by proposing an efficient framework for algorithmic testing.
Practical Batch Bayesian Optimization for Less Expensive Functions
Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions.
Bayesian functional optimisation with shape prior
Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments.
Relational dynamic memory networks
Neural networks excel in detecting regular patterns but are less successful in representing and manipulating complex data structures, possibly due to the lack of an external memory.
Variational Memory Encoder-Decoder
Introducing variability while maintaining coherence is a core task in learning to generate utterances in conversation.
Accelerated Bayesian Optimization throughWeight-Prior Tuning
In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function.
Attentional Multilabel Learning over Graphs: A Message Passing Approach
GAML regards labels as auxiliary nodes and models them in conjunction with the input graph.
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties.
High Dimensional Bayesian Optimization Using Dropout
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible.
Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present.
Dual Control Memory Augmented Neural Networks for Treatment Recommendations
The decoding controller generates a treatment sequence, one treatment option at a time.
Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records
The same hold for the bag of treatments.
Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning
One of the core tasks in multi-view learning is to capture relations among views.
Knowledge Graph Embedding with Multiple Relation Projections
Knowledge graphs contain rich relational structures of the world, and thus complement data-driven machine learning in heterogeneous data.
Graph Memory Networks for Molecular Activity Prediction
GraphMem is capable of jointly training on multiple datasets by using a specific-task query fed to the controller as an input.
Process-constrained batch Bayesian optimisation
We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy to achieve target hardness, and optimising the short polymer fibre production process.
Finding Algebraic Structure of Care in Time: A Deep Learning Approach
The interaction between diseases and treatments at a visit is modeled as the residual of the diseases minus the treatments.
Statistical Latent Space Approach for Mixed Data Modelling and Applications
The analysis of mixed data has been raising challenges in statistics and machine learning.
Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization
Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings.
Energy-based Models for Video Anomaly Detection
Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection.
Graph Classification via Deep Learning with Virtual Nodes
The representation of the virtual node is then the representation of the entire graph.
High Dimensional Bayesian Optimization with Elastic Gaussian Process
Bayesian optimization is an efficient way to optimize expensive black-box functions such as designing a new product with highest quality or hyperparameter tuning of a machine learning algorithm.
Deep Learning to Attend to Risk in ICU
At the reasoning layer, evidences across time steps are weighted and combined.
Budgeted Batch Bayesian Optimization With Unknown Batch Sizes
Current batch BO approaches are restrictive in that they fix the number of evaluations per batch, and this can be wasteful when the number of specified evaluations is larger than the number of real maxima in the underlying acquisition function.
Learning Deep Matrix Representations
We derive several new deep networks: (i) feed-forward nets that map an input matrix into an output matrix, (ii) recurrent nets which map a sequence of input matrices into a sequence of output matrices.
One Size Fits Many: Column Bundle for Multi-X Learning
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning.
Control Matching via Discharge Code Sequences
In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220, 000 patients.
Multilevel Anomaly Detection for Mixed Data
We propose MIXMAD, which stands for MIXed data Multilevel Anomaly Detection, an ensemble method that estimates the sparse regions across multiple levels of abstraction of mixed data.
Stabilizing Linear Prediction Models using Autoencoder
Using a linear model as basis for prediction, we achieve feature stability by regularising latent correlation in features.
Column Networks for Collective Classification
CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations.
Outlier Detection on Mixed-Type Data: An Energy-based Approach
We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv. RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.
Faster Training of Very Deep Networks Via p-Norm Gates
Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks.
Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data
Preterm births occur at an alarming rate of 10-15%.
Deepr: A Convolutional Net for Medical Records
On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk.
Achieving stable subspace clustering by post-processing generic clustering results
We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC).
An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments
Accurate prediction of suicide risk in mental health patients remains an open problem.
Learning deep representation of multityped objects and tasks
We introduce a deep multitask architecture to integrate multityped representations of multimodal objects.
Choice by Elimination via Deep Neural Networks
We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank.
Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to information and services.
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes.
Discovering topic structures of a temporally evolving document corpus
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.
The adaptable buffer algorithm for high quantile estimation in non-stationary data streams
The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications.
Groupwise registration of aerial images
This paper addresses the task of time separated aerial image registration.
Viewpoint distortion compensation in practical surveillance systems
Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed.
Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.
Human Activity Learning and Segmentation using Partially Hidden Discriminative Models
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance.
Boosted Markov Networks for Activity Recognition
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition.
MCMC for Hierarchical Semi-Markov Conditional Random Fields
In this contribution, we propose a new approximation technique that may have the potential to achieve sub-cubic time complexity in length and linear time depth, at the cost of some loss of quality.
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time.
Learning From Ordered Sets and Applications in Collaborative Ranking
Ranking over sets arise when users choose between groups of items.
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc.
Learning Structured Outputs from Partial Labels using Forest Ensemble
Learning structured outputs with general structures is computationally challenging, except for tree-structured models.
Permutation Models for Collaborative Ranking
Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks.
Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention.
Learning Rank Functionals: An Empirical Study
In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query.
Tree-based iterated local search for Markov random fields with applications in image analysis
The \emph{maximum a posteriori} (MAP) assignment for general structure Markov random fields (MRFs) is computationally intractable.
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters.
