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Found 19 papers, 8 papers with code
Sequential Modeling Enables Scalable Learning for Large Vision Models
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data.
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning
We use a VQ-VAE to learn state-conditioned action quantization, avoiding the exponential blowup that comes with na\"ive discretization of the action space.
Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction
In computational chemistry, crystal structure prediction (CSP) is an optimization problem that involves discovering the lowest energy stable crystal structure for a given chemical formula.
Multi-Stage Cable Routing through Hierarchical Imitation Learning
In such settings, learning individual primitives for each stage that succeed with a high enough rate to perform a complete temporally extended task is impractical: if each stage must be completed successfully and has a non-negligible probability of failure, the likelihood of successful completion of the entire task becomes negligible.
The False Promise of Imitating Proprietary LLMs
This approach looks to cheaply imitate the proprietary model's capabilities using a weaker open-source model.
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP.
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models
Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing impressive zero-shot and few-shot capabilities across a wide range of tasks.
Multimodal Masked Autoencoders Learn Transferable Representations
We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks.
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
To address this, we present Design-Bench, a benchmark for offline MBO with a unified evaluation protocol and reference implementations of recent methods.
Conservative Objective Models for Effective Offline Model-Based Optimization
Computational design problems arise in a number of settings, from synthetic biology to computer architectures.
Variable-Shot Adaptation for Incremental Meta-Learning
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
Variable-Shot Adaptation for Online Meta-Learning
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Our method is based on a simple insight: we recognize that dynamics models can be adapted efficiently and consistently with off-policy data, more easily than policies and value functions.
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
In this paper, we show that hindsight relabeling is inverse RL, an observation that suggests that we can use inverse RL in tandem for RL algorithms to efficiently solve many tasks.
Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling
Reinforcement learning algorithms can acquire policies for complex tasks automatically, however the number of samples required to learn a diverse set of skills can be prohibitively large.
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
We show that dynamical distances can be used in a semi-supervised regime, where unsupervised interaction with the environment is used to learn the dynamical distances, while a small amount of preference supervision is used to determine the task goal, without any manually engineered reward function or goal examples.
Automatic Goal Generation for Reinforcement Learning Agents
Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing.
Real-Time User-Guided Image Colorization with Learned Deep Priors
The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).
Deep Reinforcement Learning for Tensegrity Robot Locomotion
We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities.
