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Found 11 papers, 5 papers with code
Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings
The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs).
Using Forwards-Backwards Models to Approximate MDP Homomorphisms
Reinforcement learning agents must painstakingly learn through trial and error what sets of state-action pairs are value equivalent -- requiring an often prohibitively large amount of environment experience.
The CLRS Algorithmic Reasoning Benchmark
Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms.
Semantic Exploration from Language Abstractions and Pretrained Representations
We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments.
Retrieval-Augmented Reinforcement Learning
In this paper we explore an alternative paradigm in which we train a network to map a dataset of past experiences to optimal behavior.
Interpreting wide-band neural activity using convolutional neural networks
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings.
CoBERL: Contrastive BERT for Reinforcement Learning
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks.
PonderNet: Learning to Ponder
In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt.
Towards mental time travel: a hierarchical memory for reinforcement learning agents
Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks.
MEMO: A Deep Network for Flexible Combination of Episodic Memories
First, it introduces a separation between memories (facts) stored in external memory and the items that comprise these facts in external memory.
Learning to Navigate in Complex Environments
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.
