Papers
572 papers found
Fast 3D Modeling with Approximated Convolutional Kernels
Vitor Guizilini, Fabio Ramos
Stochastic Optimal Control as Approximate Input Inference
Joe Watson, Hany Abdulsamad, Jan Peters
Learning Object Manipulation Skills via Approximate State Estimation from Real Videos
Vladimír Petrík, Makarand Tapaswi, Ivan Laptev et al.
Probably Approximately Correct Vision-Based Planning using Motion Primitives
Sushant Veer, Anirudha Majumdar
Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting
Mate Soos, Kuldeep S. Meel
An Improved Quasi-Polynomial Algorithm for Approximate Well-Supported Nash Equilibria
Michail Fasoulakis, Evangelos Markakis
Approximate Inference of Outcomes in Probabilistic Elections
Batya Kenig, Benny Kimelfeld
Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
Daniel de Leng, Fredrik Heintz
EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks
Sheng-Wei Chen, Chun-Nan Chou, Edward Y. Chang
Efficient Identification of Approximate Best Configuration of Training in Large Datasets
Silu Huang, Chi Wang, Bolin Ding et al.
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli et al.
Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation
Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt
Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms
Qi Lou, Rina Dechter, Alexander Ihler
A Robust and Efficient Algorithm for the PnL Problem Using Algebraic Distance to Approximate the Reprojection Distance
Lipu Zhou, Yi Yang, Montiel Abello et al.
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling
Sanket Shah, Meghna Lowalekar, Pradeep Varakantham
SPAN: A Stochastic Projected Approximate Newton Method
Xunpeng Huang, Xianfeng Liang, Zhengyang Liu et al.
Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization
Shenghe Xu, Shivendra S. Panwar, Murali Kodialam et al.
Structure Learning for Approximate Solution of Many-Player Games
Zun Li, Michael Wellman
Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz
Gradient Boosts the Approximate Vanishing Ideal
Hiroshi Kera, Yoshihiko Hasegawa
Correcting Predictions for Approximate Bayesian Inference
Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold
Shengxi Li, Zeyang Yu, Min Xiang et al.
Stochastic Approximate Gradient Descent via the Langevin Algorithm
Yixuan Qiu, Xiao Wang
Computational Analyses of the Electoral College: Campaigning Is Hard But Approximately Manageable
Sina Dehghani, Hamed Saleh, Saeed Seddighin et al.