Search Results for author:
Found 27 papers, 16 papers with code
MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences.
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping
Developing agents that can execute multiple skills by learning from pre-collected datasets is an important problem in robotics, where online interaction with the environment is extremely time-consuming.
The CRINGE Loss: Learning what language not to model
Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples.
Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision
Finally, we train a goal-conditioned policy network with goals sampled from the goal memory and reward it by the reachability network and the goal memory.
DIRECTOR: Generator-Classifiers For Supervised Language Modeling
Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness and contradictions.
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping.
Exploration-Driven Representation Learning in Reinforcement Learning
In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space.
Staircase Attention for Recurrent Processing of Sequences
Attention mechanisms have become a standard tool for sequence modeling tasks, in particular by stacking self-attention layers over the entire input sequence as in the Transformer architecture.
Hash Layers For Large Sparse Models
We investigate the training of sparse layers that use different parameters for different inputs based on hashing in large Transformer models.
Not All Memories are Created Equal: Learning to Forget by Expiring
We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality.
Ranked #3 on
Language Modelling
on enwik8
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
In this work, we present a memory-augmented approach for image-goal navigation.
Not All Memories are Created Equal: Learning to Expire
We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve state of the art results on long-context language modeling, reinforcement learning, and algorithmic tasks.
Learning to Visually Navigate in Photorealistic Environments Without any Supervision
Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training.
Addressing Some Limitations of Transformers with Feedback Memory
Transformers have been successfully applied to sequential, auto-regressive tasks despite being feedforward networks.
Ranked #5 on
Language Modelling
on enwik8
Augmenting Self-attention with Persistent Memory
More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer.
Ranked #4 on
Language Modelling
on Text8
Training Hybrid Language Models by Marginalizing over Segmentations
In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words.
Adaptive Attention Span in Transformers
We propose a novel self-attention mechanism that can learn its optimal attention span.
Ranked #3 on
Language Modelling
on Text8
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Learning when to communicate and doing that effectively is essential in multi-agent tasks.
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Planning with Arithmetic and Geometric Attributes
A desirable property of an intelligent agent is its ability to understand its environment to quickly generalize to novel tasks and compose simpler tasks into more complex ones.
Composable Planning with Attributes
The tasks that an agent will need to solve often are not known during training.
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.
Learning Multiagent Communication with Backpropagation
Many tasks in AI require the collaboration of multiple agents.
Simple Baseline for Visual Question Answering
We describe a very simple bag-of-words baseline for visual question answering.
MazeBase: A Sandbox for Learning from Games
This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning.
End-To-End Memory Networks
For the former our approach is competitive with Memory Networks, but with less supervision.
Ranked #6 on
Question Answering
on bAbi
Training Convolutional Networks with Noisy Labels
The availability of large labeled datasets has allowed Convolutional Network models to achieve impressive recognition results.
