Search Results for author:
Found 15 papers, 9 papers with code
Improving Human Text Simplification with Sentence Fusion
The quality of fully automated text simplification systems is not good enough for use in real-world settings; instead, human simplifications are used.
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #20 on
Visual Question Answering
on MM-Vet
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM).
Large Language Models as Generalizable Policies for Embodied Tasks
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks.
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark.
Ranked #1 on
Atari Games 100k
on Atari 100k
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
To address these issues, we present reincarnating RL as an alternative workflow or class of problem settings, where prior computational work (e. g., learned policies) is reused or transferred between design iterations of an RL agent, or from one RL agent to another.
The Primacy Bias in Deep Reinforcement Learning
This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later.
Ranked #8 on
Atari Games 100k
on Atari 100k
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into $L$ simplices of $V$ dimensions each using a softmax operation.
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs.
Pretraining Representations for Data-Efficient Reinforcement Learning
Data efficiency is a key challenge for deep reinforcement learning.
Ranked #3 on
Atari Games 100k
on Atari 100k
(using extra training data)
Iterated learning for emergent systematicity in VQA
Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice.
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning
Data efficiency poses a major challenge for deep reinforcement learning.
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
Ranked #9 on
Atari Games 100k
on Atari 100k
GAIT: A Geometric Approach to Information Theory
We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities between them.
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these models while avoiding the overwhelming computational demands associated with running a statistically significant sample of simulations.
