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Found 28 papers, 15 papers with code
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
We explore how iterative revising a chain of thoughts with the help of information retrieval significantly improves large language models' reasoning and generation ability in long-horizon generation tasks, while hugely mitigating hallucination.
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Causality has been combined with machine learning to produce robust representations for domain generalization.
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options.
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective
A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to high expected return.
MCU: A Task-centric Framework for Open-ended Agent Evaluation in Minecraft
To pursue the goal of creating an open-ended agent in Minecraft, an open-ended game environment with unlimited possibilities, this paper introduces a task-centric framework named MCU for Minecraft agent evaluation.
GROOT: Learning to Follow Instructions by Watching Gameplay Videos
We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities.
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
In this paper, we theoretically and empirically discover that the performance of a PC can exceed that of its teacher model.
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
We investigate the challenge of task planning for multi-task embodied agents in open-world environments.
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents.
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning.
RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding
In this paper, we propose a novel and principled framework called \textbf{RulE} (stands for {Rul}e {E}mbedding) to effectively leverage logical rules to enhance KG reasoning.
SQA3D: Situated Question Answering in 3D Scenes
We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D).
Ranked #1 on
Referring Expression
on SQA3D
Neural-Symbolic Recursive Machine for Systematic Generalization
In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning.
Towards an Interpretable Latent Space in Structured Models for Video Prediction
We work with models which are object-centric, i. e., explicitly work with object representations, and propagate a loss in the latent space.
Handling Missing Data in Decision Trees: A Probabilistic Approach
As such, handling missing data in decision trees is a well studied problem.
On Effective Parallelization of Monte Carlo Tree Search
Second, and more importantly, we demonstrate how the proposed necessary conditions can be adopted to design more effective parallel MCTS algorithms.
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration
Off-policy reinforcement learning (RL) is concerned with learning a rewarding policy by executing another policy that gathers samples of experience.
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning
To learn this representation, we train a squeeze network to drive using annotations for the side task as input.
On Tractable Computation of Expected Predictions
In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations, as well as moments of any order, of the latter with respect to the former in case of regression.
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge.
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform.
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels.
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
This paper develops a novel methodology for using symbolic knowledge in deep learning.
State of the Art Control of Atari Games Using Shallow Reinforcement Learning
The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning.
