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Found 15 papers, 5 papers with code
Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces
In the tabular case, \citet{rowland2018analysis} and \citet{rowland2023analysis} proved the asymptotic convergence of two instances of distributional TD, namely categorical temporal difference algorithm (CTD) and quantile temporal difference algorithm (QTD), respectively.
Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey
Object detection and tracking are vital and fundamental tasks for autonomous driving, aiming at identifying and locating objects from those predefined categories in a scene.
Estimation and Inference in Distributional Reinforcement Learning
This implies the distributional policy evaluation problem can be solved with sample efficiency.
Distributional Reinforcement Learning
reinforcement-learning
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture
The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models.
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
To the best of our knowledge, we are the first to apply tools from semi-infinitely programming (SIP) to solve constrained reinforcement learning problems.
Finding Lookalike Customers for E-Commerce Marketing
Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart.
Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph
Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals.
Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion
Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge.
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e. g., up to (much greater than) 100 FPs generated, or up to approx.
Federated Reinforcement Learning with Environment Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction.
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value.
Domain Adaptive Person Re-Identification
Person Re-Identification
Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision
Convolutional neural networks (CNNs) have become an established part of numerous safety-critical computer vision applications, including human robot interactions and automated driving.
Bioinspired Bipedal Locomotion Control for Humanoid Robotics Based on EACO
To construct a robot that can walk as efficiently and steadily as humans or other legged animals, we develop an enhanced elitist-mutated ant colony optimization~(EACO) algorithm with genetic and crossover operators in real-time applications to humanoid robotics or other legged robots.
To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI
One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science while developing brain-inspired artificial intelligence platforms that will promote new scientific discoveries.
