Search Results for author:
Found 14 papers, 5 papers with code
Decomposed Mutual Information Estimation for Contrastive Representation Learning
We propose decomposing the full MI estimation problem into a sum of smaller estimation problems by splitting one of the views into progressively more informed subviews and by applying the chain rule on MI between the decomposed views.
Decomposing Mutual Information for Representation Learning
In this paper, we transform each view into a set of subviews and then decompose the original MI bound into a sum of bounds involving conditional MI between the subviews.
Towards a Scalable and Distributed Infrastructure for Deep Learning Applications
Although recent scaling up approaches to training deep neural networks have proven to be effective, the computational intensity of large and complex models, as well as the availability of large-scale datasets, require deep learning frameworks to utilize scaling out techniques.
Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation
Domain adaptation has recently become a key problem in dialogue systems research.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Towards Deep Conversational Recommendations
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems.
From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Conditional text-to-image generation is an active area of research, with many possible applications.
Ranked #2 on
Text-to-Image Generation
on GeNeVA (i-CLEVR)
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting.
A Frame Tracking Model for Memory-Enhanced Dialogue Systems
Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems.
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
We developed this dataset to study the role of memory in goal-oriented dialogue systems.
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data
Natural language generation plays a critical role in spoken dialogue systems.
Policy Networks with Two-Stage Training for Dialogue Systems
Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL.
