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Found 32 papers, 18 papers with code
EpiK-Eval: Evaluation for Language Models as Epistemic Models
In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central.
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging.
TorchRL: A data-driven decision-making library for PyTorch
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments.
When should we prefer Decision Transformers for Offline Reinforcement Learning?
Three popular algorithms for offline RL are Conservative Q-Learning (CQL), Behavior Cloning (BC), and Decision Transformer (DT), from the class of Q-Learning, Imitation Learning, and Sequence Modeling respectively.
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question.
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks
Our theoretical understanding of deep learning has not kept pace with its empirical success.
An Introduction to Lifelong Supervised Learning
Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.
Robust Policy Learning over Multiple Uncertainty Sets
Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments.
Block Contextual MDPs for Continual Learning
In this work, we propose to examine this continual reinforcement learning setting through the block contextual MDP (BC-MDP) framework, which enables us to relax the assumption of stationarity.
Multi-Task Reinforcement Learning with Context-based Representations
We posit that an efficient approach to knowledge transfer is through the use of multiple context-dependent, composable representations shared across a family of tasks.
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks
In this work, we study the logical generalization capabilities of GNNs by designing a benchmark suite grounded in first-order logic.
IIRC: Incremental Implicitly-Refined Classification
We develop a standardized benchmark that enables evaluating models on the IIRC setup.
A Closer Look at Codistillation for Distributed Training
Surprisingly, we find that even at moderate batch sizes, models trained with codistillation can perform as well as models trained with synchronous data-parallel methods, despite using a much weaker synchronization mechanism.
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019.
Learning Robust State Abstractions for Hidden-Parameter Block MDPs
Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work that use the same environment assumptions.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
Evaluating Logical Generalization in Graph Neural Networks
Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner.
Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.
Recurrent Independent Mechanisms
Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes.
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CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Learning Powerful Policies by Using Consistent Dynamics Model
There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment.
Environments for Lifelong Reinforcement Learning
To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned.
Towards Training Recurrent Neural Networks for Lifelong Learning
Both these models are proposed in the context of feedforward networks and we evaluate the feasibility of using them for recurrent networks.
Compositional Language Understanding with Text-based Relational Reasoning
Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference.
Spatial Co-location Pattern Mining - A new perspective using Graph Database
Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places.
Databases Distributed, Parallel, and Cluster Computing
Learning powerful policies and better dynamics models by encouraging consistency
Analogously, we would expect such interaction to be helpful for a learning agent while learning to model the environment dynamics.
Memory Augmented Self-Play
Self-play is an unsupervised training procedure which enables the reinforcement learning agents to explore the environment without requiring any external rewards.
Reproducibility Report for "Learning To Count Objects In Natural Images For Visual Question Answering"
This is the reproducibility report for the paper "Learning To Count Objects In Natural Images For Visual QuestionAnswering"
Survey of Recent Advances in Visual Question Answering
Visual Question Answering (VQA) presents a unique challenge as it requires the ability to understand and encode the multi-modal inputs - in terms of image processing and natural language processing.
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent.
Stack Exchange Tagger
The goal of our project is to develop an accurate tagger for questions posted on Stack Exchange.
