Search Results for author:
Found 144 papers, 58 papers with code
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
Parameterized Rate-Distortion Stochastic Encoder
We propose a novel gradient-based tractable approach for the Blahut-Arimoto (BA) algorithm to compute the rate-distortion function where the BA algorithm is fully parameterized.
Connective Viewpoints of Signal-to-Noise Diffusion Models
Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpolation.
MetaAug: Meta-Data Augmentation for Post-Training Quantization
The transformation network modifies the original calibration data and the modified data will be used as the training set to learn the quantized model with the objective that the quantized model achieves a good performance on the original calibration data.
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions.
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
Topic modeling has been a widely used tool for unsupervised text analysis.
Agnostic Sharpness-Aware Minimization
Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, leading the model into flatter minima that are associated with better generalization properties.
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction
Personalizing a large-scale pretrained Text-to-Image (T2I) diffusion model is challenging as it typically struggles to make an appropriate trade-off between its training data distribution and the target distribution, i. e., learning a novel concept with only a few target images to achieve personalization (aligning with the personalized target) while preserving text editability (aligning with diverse text prompts).
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitating subsequent matching.
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.
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
In this field, Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems.
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers
There has long been plenty of theoretical and empirical evidence supporting the success of ensemble learning.
Removing Undesirable Concepts in Text-to-Image Diffusion Models with Learnable Prompts
By transferring this knowledge to the prompt, erasing undesirable concepts becomes more stable and has minimal negative impact on other concepts.
Frequency Attention for Knowledge Distillation
Inspired by the benefits of the frequency domain, we propose a novel module that functions as an attention mechanism in the frequency domain.
Optimal Transport for Structure Learning Under Missing Data
To address this problem, we propose a score-based algorithm for learning causal structures from missing data based on optimal transport.
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation
Additionally, we propose minimizing class-aware Higher-order Moment Matching (HMM) to align the corresponding class regions on the source and target domains.
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning
To address this, we propose the Gradient Projection Class-Prototype Conditional Diffusion Model (GPPDM), a GR-based approach for continual learning that enhances image quality in generators and thus reduces the CF in classifiers.
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All
Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field of Continual Learning.
Robust Contrastive Learning With Theory Guarantee
Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information.
PhoGPT: Generative Pre-training for Vietnamese
The base model, PhoGPT-4B, with exactly 3. 7B parameters, is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length, employing a vocabulary of 20480 token types.
Systematic Assessment of Factual Knowledge in Large Language Models
We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains.
Cross-adversarial local distribution regularization for semi-supervised medical image segmentation
Medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data.
NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation
In this paper, we propose a novel Noisy Layer Generation method (NAYER) which relocates the random source from the input to a noisy layer and utilizes the meaningful constant label-text embedding (LTE) as the input.
RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization
Motivated by this analysis, we introduce our algorithm, Riemannian Sharpness-Aware Minimization (RSAM).
Towards Generalising Neural Topical Representations
To do so, we propose to enhance NTMs by narrowing the semantic distance between similar documents, with the underlying assumption that documents from different corpora may share similar semantics.
Optimal Transport Model Distributional Robustness
Interestingly, our developed theories allow us to flexibly incorporate the concept of sharpness awareness into training, whether it's a single model, ensemble models, or Bayesian Neural Networks, by considering specific forms of the center model distribution.
Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities
Prior studies found that vulnerabilities across different vulnerable programs may exhibit similar vulnerable scopes, implicitly forming discernible vulnerability patterns that can be learned by DL models through supervised training.
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge.
Sharpness & Shift-Aware Self-Supervised Learning
Self-supervised learning aims to extract meaningful features from unlabeled data for further downstream tasks.
Active Continual Learning: On Balancing Knowledge Retention and Learnability
Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL).
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
The key factor for the success of adversarial training is the capability to generate qualified and divergent adversarial examples which satisfy some objectives/goals (e. g., finding adversarial examples that maximize the model losses for simultaneously attacking multiple models).
Hyperbolic Geometry in Computer Vision: A Survey
Hyperbolic geometry, a Riemannian manifold endowed with constant sectional negative curvature, has been considered an alternative embedding space in many learning scenarios, \eg, natural language processing, graph learning, \etc, as a result of its intriguing property of encoding the data's hierarchical structure (like irregular graph or tree-likeness data).
Vector Quantized Wasserstein Auto-Encoder
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks.
Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation
Recent mask proposal models have significantly improved the performance of zero-shot semantic segmentation.
Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance
On the other hand, in order to achieve that, we need to devise even stronger adversarial attacks to challenge these defense models.
Continual Learning with Optimal Transport based Mixture Model
To address this issue, in this work, we first propose an online mixture model learning approach based on nice properties of the mature optimal transport theory (OT-MM).
Improving Multi-task Learning via Seeking Task-based Flat Regions
Multi-Task Learning (MTL) is a widely-used and powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?
Our approach departs from the computational process of ViTs with a focus on visualizing the local and global information in input images and the latent feature embeddings at multiple levels.
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability.
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learning
In this paper, we propose a novel end-to-end deep learning-based approach to identify the vulnerability-relevant code statements of a specific function.
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle
However, there are still two open and significant issues for SVD in terms of i) learning automatic representations to improve the predictive performance of SVD, and ii) tackling the scarcity of labeled vulnerabilities datasets that conventionally need laborious labeling effort by experts.
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches within an image into the same index, resulting in a repeated artifact for similar adjacent regions using existing decoder architectures.
An Additive Instance-Wise Approach to Multi-class Model Interpretation
A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner.
Stochastic Multiple Target Sampling Gradient Descent
Furthermore, when analysing its asymptotic properties, SVGD reduces exactly to a single-objective optimization problem and can be viewed as a probabilistic version of this single-objective optimization problem.
High-Quality Pluralistic Image Completion via Code Shared VQGAN
In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed.
Global-Local Regularization Via Distributional Robustness
First, they purely focus on local regularization to strengthen model robustness, missing a global regularization effect which is useful in many real-world applications (e. g., domain adaptation, domain generalization, and adversarial machine learning).
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
We introduce a new Wasserstein cost function and a new series of risk functions, with which we show that standard AT methods are special cases of their counterparts in our framework.
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
In this work, we consider probability measures supported on a graph metric space and propose a novel Sobolev transport metric.
Two-view Graph Neural Networks for Knowledge Graph Completion
WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes.
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e. g., learning domain-invariant representations and its trade-off.
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks
To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment.
On Label Shift in Domain Adaptation via Wasserstein Distance
We study the label shift problem between the source and target domains in general domain adaptation (DA) settings.
Exploiting Domain-Specific Features to Enhance Domain Generalization
We empirically show that mDSDI provides competitive results with state-of-the-art techniques in DG.
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection
Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
Improving Robustness with Optimal Transport based Adversarial Generalization
More specifically, by minimizing the WS distance of interest, an adversarial example is pushed toward the cluster of benign examples sharing the same label on the latent space, which helps to strengthen the generalization ability of the classifier on the adversarial examples.
Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning
Software vulnerabilities existing in a program or function of computer systems have been becoming a serious and crucial concern.
Contrastively Enforcing Distinctiveness for Multi-Label Classification
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains.
LASSO: Latent Sub-spaces Orientation for Domain Generalization
To achieve a satisfactory generalization performance on prediction tasks in an unseen domain, existing domain generalization (DG) approaches often rely on the strict assumption of fixed domain-invariant features and common hypotheses learned from a set of training domains.
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
Rapid Elastic Architecture Search under Specialized Classes and Resource Constraints
More critically, EAS is able to find compact architectures within 0. 1 second for 50 deployment scenarios.
Multi-Label Image Classification with Contrastive Learning
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning.
Imitation Learning
Multi-Source Unsupervised Domain Adaptation
+1
Text Generation with Deep Variational GAN
Generating realistic sequences is a central task in many machine learning applications.
Improved and Efficient Text Adversarial Attacks using Target Information
There has been recently a growing interest in studying adversarial examples on natural language models in the black-box setting.
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i. e., link prediction).
Bridging Global Context Interactions for High-Fidelity Image Completion
Bridging global context interactions correctly is important for high-fidelity image completion with large masks.
Ranked #2 on
Image Inpainting
on FFHQ 512 x 512
Topic Modelling Meets Deep Neural Networks: A Survey
Topic modelling has been a successful technique for text analysis for almost twenty years.
On Transportation of Mini-batches: A Hierarchical Approach
To address these problems, we propose a novel mini-batch scheme for optimal transport, named Batch of Mini-batches Optimal Transport (BoMb-OT), that finds the optimal coupling between mini-batches and it can be seen as an approximation to a well-defined distance on the space of probability measures.
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Central to this approach is the selection of positive (similar) and negative (dissimilar) sets to provide the model the opportunity to `contrast' between data and class representation in the latent space.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
We present an optimal transport framework for learning topics from textual data.
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering
Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests.
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
Training robust deep learning models for down-stream tasks is a critical challenge.
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs.
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
An important technique of this approach is to control the transferability of adversarial examples among ensemble members.
Quaternion Graph Neural Networks
As demonstrated, the Quaternion space, a hyper-complex vector space, provides highly meaningful computations and analogical calculus through Hamilton product compared to the Euclidean and complex vector spaces.
Neural Topic Model via Optimal Transport
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis.
Ranked #5 on
Topic Models
on 20NewsGroups
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models
To interpret the teacher model and assist the learning of the student, an explainer module is introduced to highlight the regions of an input that are important for the predictions of the teacher model.
Improving Adversarial Robustness by Enforcing Local and Global Compactness
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application.
A Self-Attention Network based Node Embedding Model
Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes -- a setting encountered commonly in practical applications of deep learning for graph networks.
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals.
A Capsule Network-based Model for Learning Node Embeddings
In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data.
Ranked #52 on
Node Classification
on Pubmed
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree metrics, for Wasserstein distance.
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.
Universal Graph Transformer Self-Attention Networks
The transformer self-attention network has been extensively used in research domains such as computer vision, image processing, and natural language processing.
Ranked #1 on
Graph Classification
on IMDb-M
Unsupervised Universal Self-Attention Network for Graph Classification
Thus, U2GAN can address the weaknesses in the existing models in order to produce plausible node embeddings whose sum is the final embedding of the whole graph.
An effective spatial-temporal attention based neural network for traffic flow prediction
In this study, a deep learning based traffic flow predictor with spatial and temporal attentions (STANN) is proposed.
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems.
Learning How to Active Learn by Dreaming
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security.
When Can Neural Networks Learn Connected Decision Regions?
Previous work has questioned the conditions under which the decision regions of a neural network are connected and further showed the implications of the corresponding theory to the problem of adversarial manipulation of classifiers.
On Deep Domain Adaptation: Some Theoretical Understandings
The underlying idea of deep domain adaptation is to bridge the gap between source and target domains in a joint space so that a supervised classifier trained on labeled source data can be nicely transferred to the target domain.
Probabilistic Multilevel Clustering via Composite Transportation Distance
We propose a novel probabilistic approach to multilevel clustering problems based on composite transportation distance, which is a variant of transportation distance where the underlying metric is Kullback-Leibler divergence.
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object).
Ranked #41 on
Link Prediction
on WN18RR
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models
Abnormal event detection is one of the important objectives in research and practical applications of video surveillance.
A Capsule Network-based Embedding Model for Search Personalization
After that, the 3-column matrix is fed into a deep learning architecture to re-rank the search results returned by a basis ranker.
MGAN: Training Generative Adversarial Nets with Multiple Generators
We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps.
Ranked #59 on
Link Prediction
on WN18RR
KGAN: How to Break The Minimax Game in GAN
In this paper, we propose a new viewpoint for GANs, which is termed as the minimizing general loss viewpoint.
Analogical-based Bayesian Optimization
With this spirit, in this paper, we propose Analogical-based Bayesian Optimization that can maximize black-box function over a domain where only a similarity score can be defined.
Dual Discriminator Generative Adversarial Nets
We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem.
Ranked #18 on
Image Generation
on STL-10
(Inception score metric)
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.
Statistical Latent Space Approach for Mixed Data Modelling and Applications
The analysis of mixed data has been raising challenges in statistics and machine learning.
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.
Geometric Enclosing Networks
Training model to generate data has increasingly attracted research attention and become important in modern world applications.
Multi-Generator Generative Adversarial Nets
A minimax formulation is able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN.
Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.
Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric
Multiple instance data are sets or multi-sets of unordered elements.
A Random Finite Set Model for Data Clustering
The goal of data clustering is to partition data points into groups to minimize a given objective function.
Model-Based Multiple Instance Learning
While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning.
Clustering For Point Pattern Data
However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources.
Model-based Classification and Novelty Detection For Point Pattern Data
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources.
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.
Dual Space Gradient Descent for Online Learning
However, this approach still suffers from a serious shortcoming as it needs to use a high dimensional random feature space to achieve a sufficiently accurate kernel approximation.
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%.
Scalable Semi-supervised Learning with Graph-based Kernel Machine
Acquiring labels are often costly, whereas unlabeled data are usually easy to obtain in modern machine learning applications.
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.
Approximation Vector Machines for Large-scale Online Learning
One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity.
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.
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.
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.
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc.
Learning From Ordered Sets and Applications in Collaborative Ranking
Ranking over sets arise when users choose between groups of items.
Learning Structured Outputs from Partial Labels using Forest Ensemble
Learning structured outputs with general structures is computationally challenging, except for tree-structured models.
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.
