research
∙
06/07/2023
Multimodal Fusion Interactions: A Study of Human and Automatic Quantification
Multimodal fusion of multiple heterogeneous and interconnected signals i...
share
research
∙
05/03/2023
Plan, Eliminate, and Track – Language Models are Good Teachers for Embodied Agents
Pre-trained large language models (LLMs) capture procedural knowledge ab...
research
∙
02/23/2023
Quantifying Modeling Feature Interactions: An Information Decomposition Framework
The recent explosion of interest in multimodal applications has resulted...
research
∙
01/31/2023
Grounding Language Models to Images for Multimodal Generation
We propose an efficient method to ground pretrained text-only language m...
research
∙
06/21/2022
A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search
Physically rearranging objects is an important capability for embodied a...
research
∙
06/05/2021
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
Self-supervised learning is a form of unsupervised learning that leverag...
research
∙
06/01/2021
End-to-End Multihop Retrieval for Compositional Question Answering over Long Documents
Answering complex questions from long documents requires aggregating mul...
research
∙
10/09/2020
Case Study: Deontological Ethics in NLP
Recent work in natural language processing (NLP) has focused on ethical ...
research
∙
09/08/2020
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
In this paper, we study bidirectional LSTM network for the task of text ...
research
∙
06/10/2020
Demystifying Self-Supervised Learning: An Information-Theoretical Framework
Self-supervised representation learning adopts self-defined signals as s...
research
∙
05/04/2020
Exploring Controllable Text Generation Techniques
Neural controllable text generation is an important area gaining attenti...
research
∙
10/22/2019
Complex Transformer: A Framework for Modeling Complex-Valued Sequence
While deep learning has received a surge of interest in a variety of fie...
research
∙
07/29/2019
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
The sample inefficiency of standard deep reinforcement learning methods ...
research
∙
06/29/2019
Deep Gamblers: Learning to Abstain with Portfolio Theory
We deal with the selective classification problem (supervised-learning p...
research
∙
06/12/2019
Efficient Exploration via State Marginal Matching
To solve tasks with sparse rewards, reinforcement learning algorithms mu...
research
∙
04/26/2019
On Exact Computation with an Infinitely Wide Neural Net
How well does a classic deep net architecture like AlexNet or VGG19 clas...
research
∙
01/09/2019
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformer networks have a potential of learning longer-term dependency...
research
∙
06/16/2018
Learning Factorized Multimodal Representations
Learning representations of multimodal data is a fundamentally complex r...
research
∙
05/21/2018
How Many Samples are Needed to Learn a Convolutional Neural Network?
A widespread folklore for explaining the success of convolutional neural...
research
∙
01/19/2018
Investigating the Working of Text Classifiers
Text classification is one of the most widely studied task in natural la...
research
∙
07/16/2017
Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training
In this paper, we propose a generic and simple algorithmic framework for...
research
∙
03/10/2017
Deep Sets
In this paper, we study the problem of designing objective functions for...
research
∙
11/09/2015
Generating Images from Captions with Attention
Motivated by the recent progress in generative models, we introduce a mo...
research
∙
02/10/2015
