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Found 21 papers, 6 papers with code
ELA: Efficient Local Attention for Deep Convolutional Neural Networks
The attention mechanism has gained significant recognition in the field of computer vision due to its ability to effectively enhance the performance of deep neural networks.
A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays
In this paper, we study asynchronous stochastic approximation algorithms without communication delays.
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
Survey on video anomaly detection in dynamic scenes with moving cameras
The increasing popularity of compact and inexpensive cameras, e. g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras.
Attentive Multimodal Fusion for Optical and Scene Flow
This paper presents an investigation into the estimation of optical and scene flow using RGBD information in scenarios where the RGB modality is affected by noise or captured in dark environments.
Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds
The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss.
On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly Communicating MDPs
We show two average-reward off-policy control algorithms, Differential Q-learning (Wan, Naik, & Sutton 2021a) and RVI Q-learning (Abounadi Bertsekas & Borkar 2001), converge in weakly communicating MDPs.
Toward Discovering Options that Achieve Faster Planning
In a variant of the classic four-room domain, we show that 1) a higher objective value is typically associated with fewer number of elementary planning operations used by the option-value iteration algorithm to obtain a near-optimal value function, 2) our algorithm achieves an objective value that matches it achieved by two human-designed options 3) the amount of computation used by option-value iteration with options discovered by our algorithm matches it with the human-designed options, 4) the options produced by our algorithm also make intuitive sense--they seem to move to and terminate at the entrances of rooms.
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes.
Model-based Reinforcement Learning
reinforcement-learning
+1
LiDAR-guided Stereo Matching with a Spatial Consistency Constraint
Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness.
ELSR: Efficient Line Segment Reconstruction With Planes and Points Guidance
In this paper, we propose an efficient line segment reconstruction method called ELSR.
Loop closure detection using local 3D deep descriptors
We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-the-art loop closure detection accuracy.
Average-Reward Learning and Planning with Options
We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs.
Planning with Expectation Models for Control
Our algorithms and experiments are the first to treat MBRL with expectation models in a general setting.
Average-Reward Off-Policy Policy Evaluation with Function Approximation
We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value function.
Detecting Log Anomalies with Multi-Head Attention (LAMA)
Anomaly detection is a crucial and challenging subject that has been studied within diverse research areas.
Incremental Policy Gradients for Online Reinforcement Learning Control
Our emphasis is on the first approach in this work, detailing an incremental policy gradient update which neither waits until the end of the episode, nor relies on learning estimates of the return.
Learning and Planning in Average-Reward Markov Decision Processes
We introduce learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference states, 2) the first proven-convergent off-policy model-free prediction algorithm, and 3) the first off-policy learning algorithm that converges to the actual value function rather than to the value function plus an offset.
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
A common way to solve this problem, known as Mixture-of-Experts, is to represent the policy as the weighted sum of multiple components, where different components perform well on different parts of the state space.
Planning with Expectation Models
In particular, we 1) show that planning with an expectation model is equivalent to planning with a distribution model if the state value function is linear in state features, 2) analyze two common parametrization choices for approximating the expectation: linear and non-linear expectation models, 3) propose a sound model-based policy evaluation algorithm and present its convergence results, and 4) empirically demonstrate the effectiveness of the proposed planning algorithm.
Clustering Assisted Fundamental Matrix Estimation
In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied.
