Search Results for author:
Found 21 papers, 14 papers with code
Time-Aware Item Weighting for the Next Basket Recommendations
In addition, we show the results of an ablation study and a case study of a few items.
RecBaselines2023: a new dataset for choosing baselines for recommender models
To solve this problem, we have collected and published a dataset containing information about the recommender models used in 903 papers, both as baselines and as proposed approaches.
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection.
Revisiting the Minimalist Approach to Offline Reinforcement Learning
In this work, we aim to bridge this gap by conducting a retrospective analysis of recent works in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such design elements built on top of the TD3+BC method.
Anti-Exploration by Random Network Distillation
Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning.
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning.
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks.
CORL: Research-oriented Deep Offline Reinforcement Learning Library
CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
Deep Image Retrieval is not Robust to Label Noise
Large-scale datasets are essential for the success of deep learning in image retrieval.
EXACT: How to Train Your Accuracy
Classification tasks are usually evaluated in terms of accuracy.
Ranked #2 on
Image Classification
on SVHN
(Percentage correct metric)
CVTT: Cross-Validation Through Time
The evaluation of recommender systems from a practical perspective is a topic of ongoing discourse within the research community.
Probabilistic Embeddings Revisited
We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research.
Next Period Recommendation Reality Check
In this article, we aim to fill the gap in RecSys methods evaluation on the NPR task using publicly available datasets and (1) introduce the TTRS, a large-scale financial transactions dataset suitable for RecSys methods evaluation; (2) benchmark popular RecSys approaches on several datasets for the NPR task.
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters
In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms.
LRWR: Large-Scale Benchmark for Lip Reading in Russian language
Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas.
Sample Efficient Ensemble Learning with Catalyst.RL
We present Catalyst. RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research.
Catalyst.RL: A Distributed Framework for Reproducible RL Research
Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing different RL algorithms.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
Run, skeleton, run: skeletal model in a physics-based simulation
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible.
