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Found 22 papers, 14 papers with code
Amplifying human performance in combinatorial competitive programming
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors.
Quantum Circuit Optimization with AlphaTensor
A key challenge in realizing fault-tolerant quantum computers is circuit optimization.
Discovering faster matrix multiplication algorithms with reinforcement learning
Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2.
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs.
Ranked #1 on
Skill Generalization
on RGB-Stacking
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making.
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds
In scientific computing and machine learning applications, matrices and more general multidimensional arrays (tensors) can often be approximated with the help of low-rank decompositions.
Semi-supervised reward learning for offline reinforcement learning
In offline reinforcement learning (RL) agents are trained using a logged dataset.
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Offline Learning from Demonstrations and Unlabeled Experience
Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations.
Sydr: Cutting Edge Dynamic Symbolic Execution
Sydr performs inversion of branches in path predicate.
Cryptography and Security
Hyperparameter Selection for Offline Reinforcement Learning
Therefore, in this work, we focus on \textit{offline hyperparameter selection}, i. e. methods for choosing the best policy from a set of many policies trained using different hyperparameters, given only logged data.
Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Task-Relevant Adversarial Imitation Learning
We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels.
Scaling data-driven robotics with reward sketching and batch reinforcement learning
We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions.
Visual Imitation with a Minimal Adversary
The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.
Tensor Train decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learning.
Mathematical Software Numerical Analysis
Expressive power of recurrent neural networks
In this paper, we prove the expressive power theorem (an exponential lower bound on the width of the equivalent shallow network) for a class of recurrent neural networks -- ones that correspond to the Tensor Train (TT) decomposition.
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models.
Ultimate tensorization: compressing convolutional and FC layers alike
Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity.
Exponential Machines
Modeling interactions between features improves the performance of machine learning solutions in many domains (e. g. recommender systems or sentiment analysis).
Tensorizing Neural Networks
Deep neural networks currently demonstrate state-of-the-art performance in several domains.
Ranked #53 on
Image Classification
on MNIST
