research
∙
05/25/2023
Markov Decision Process with an External Temporal Process
Most reinforcement learning algorithms treat the context under which the...
share
research
∙
01/28/2023
Neural Temporal Point Process for Forecasting Higher Order and Directional Interactions
Real-world systems are made of interacting entities that evolve with tim...
research
∙
03/03/2022
On consistency of constrained spectral clustering under representation-aware stochastic block model
Spectral clustering is widely used in practice due to its flexibility, c...
research
∙
12/21/2021
An Inference Approach To Question Answering Over Knowledge Graphs
Knowledge Graphs (KG) act as a great tool for holding distilled informat...
research
∙
12/19/2021
Representation Learning for Dynamic Hyperedges
Recently there has been a massive interest in extracting information fro...
research
∙
12/17/2021
ActKnow: Active External Knowledge Infusion Learning for Question Answering in Low Data Regime
Deep learning models have set benchmark results in various Natural Langu...
research
∙
12/02/2021
Risk-Aware Algorithms for Combinatorial Semi-Bandits
In this paper, we study the stochastic combinatorial multi-armed bandit ...
research
∙
05/08/2021
Protecting Individual Interests across Clusters: Spectral Clustering with Guarantees
Studies related to fairness in machine learning have recently gained tra...
research
∙
03/11/2021
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
While deep learning is a powerful tool for natural language processing (...
research
∙
01/30/2021
Stay Alive with Many Options: A Reinforcement Learning Approach for Autonomous Navigation
Hierarchical reinforcement learning approaches learn policies based on h...
research
∙
12/24/2020
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints
The multi-armed bandits' framework is the most common platform to study ...
research
∙
11/05/2020
Adversarial Context Aware Network Embeddings for Textual Networks
Representation learning of textual networks poses a significant challeng...
research
∙
05/25/2020
Contradistinguisher: Applying Vapnik's Philosophy to Unsupervised Domain Adaptation
A complex combination of simultaneous supervised-unsupervised learning i...
research
∙
04/06/2020
Networked Multi-Agent Reinforcement Learning with Emergent Communication
Multi-Agent Reinforcement Learning (MARL) methods find optimal policies ...
research
∙
11/26/2019
A Statistical Model for Dynamic Networks with Neural Variational Inference
In this paper we propose a statistical model for dynamically evolving ne...
research
∙
11/11/2019
Restricted Boltzmann Stochastic Block Model: A Generative Model for Networks with Attributes
In most practical contexts network indexed data consists not only of a d...
research
∙
11/01/2019
Active Learning with Siamese Twins for Sequence Tagging
Deep learning, in general, and natural language processing methods, in p...
research
∙
09/08/2019
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
In this paper, we propose a simple model referred as Contradistinguisher...
research
∙
02/19/2019
On Voting Strategies and Emergent Communication
Humans use language to collectively execute complex strategies in additi...
research
∙
11/17/2018
Deep Discriminative Learning for Unsupervised Domain Adaptation
The primary objective of domain adaptation methods is to transfer knowle...
research
∙
02/16/2018
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing
Supervised learning models are typically trained on a single dataset and...
research
∙
02/16/2018
Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
Machine Learning has been the quintessential solution for many AI proble...
research
∙
02/11/2018
Evolving Latent Space Model for Dynamic Networks
Networks observed in the real world like social networks, collaboration ...
research
∙
05/03/2017
Amortized Inference and Learning in Latent Conditional Random Fields for Weakly-Supervised Semantic Image Segmentation
Conditional random fields (CRFs) are commonly employed as a post-process...
research
∙
03/23/2017
Generative Adversarial Residual Pairwise Networks for One Shot Learning
Deep neural networks achieve unprecedented performance levels over many ...
research
∙
03/02/2017
Attentive Recurrent Comparators
Rapid learning requires flexible representations to quickly adopt to new...
research
∙
02/12/2017
Spectral Clustering via Graph Filtering: Consistency on the High-Dimensional Stochastic Block Model
Spectral clustering is amongst the most popular methods for community de...
research
∙
01/17/2017
Image Generation and Editing with Variational Info Generative AdversarialNetworks
Recently there has been an enormous interest in generative models for im...
research
∙
11/16/2016
Deep Variational Inference Without Pixel-Wise Reconstruction
Variational autoencoders (VAEs), that are built upon deep neural network...
research
∙
11/15/2016
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
In this work we use the recent advances in representation learning to pr...
research
∙
03/06/2016
Variational methods for Conditional Multimodal Deep Learning
In this paper, we address the problem of conditional modality learning, ...
research
∙
02/21/2016
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
In a series of recent works, we have generalised the consistency results...
research
∙
02/13/2016
On Gröbner Bases and Krull Dimension of Residue Class Rings of Polynomial Rings over Integral Domains
Given an ideal a in A[x_1, ..., x_n], where A is a Noetherian integral d...
research
∙
05/07/2015
Consistency of Spectral Hypergraph Partitioning under Planted Partition Model
Hypergraph partitioning lies at the heart of a number of problems in mac...
research
∙
04/09/2012
