Résultats de la recherche - (coding OR decoding) (algorithm OR (Algorithmsssics OR Algorithmsssics))

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning par Nakamoto, Mitsuhiko, Zhai, Yuexiang, Singh, Anikait, Mark, Max Sobol, Ma, Yi, Finn, Chelsea, Kumar, Aviral, Levine, Sergey

Table des matières: “… simply be the behavior policy. We show that offline RL algorithms that learn such calibrated value…”
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Partially Fault-tolerant Quantum Computing Architecture with Error-corrected Clifford Gates and Space-time Efficient Analog Rotations par Akahoshi, Yutaro, Maruyama, Kazunori, Oshima, Hirotaka, Sato, Shintaro, Fujii, Keisuke

Table des matières: “… (FTQC) based on the quantum error correction (QEC) code remains far beyond realization due to its…”
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Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation par Su, Sanbao, Han, Songyang, Li, Yiming, Zhang, Zhili, Feng, Chen, Ding, Caiwen, Miao, Fei

Table des matières: “… collaborative object detectors and baseline MOT algorithms. We evaluate MOT-CUP on V2X-Sim, a comprehensive…”
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HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices par Toupas, Petros, Montgomerie-Corcoran, Alexander, Bouganis, Christos-Savvas, Tzovaras, Dimitrios

Table des matières: “… model, iii) a scheduling algorithm for executing 3D models on the generated hardware, iv) a resource…”
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SCART: Simulation of Cyber Attacks for Real-Time par Rahimi, Eliron, Girstein, Kfir, Malits, Roman, Mendelson, Avi

Table des matières: “… algorithms. These advancements hold the potential to significantly enhance anomaly detection in real-time…”
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Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data par Zhou, Hongkuan, Bing, Zhenshan, Yao, Xiangtong, Su, Xiaojie, Yang, Chenguang, Huang, Kai, Knoll, Alois

Table des matières: “… skill priors and imitation learning under unstructured data to enhance the algorithm's generalization…”
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Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration par Kim, Dongyoung, Shin, Jinwoo, Abbeel, Pieter, Seo, Younggyo

Table des matières: “… algorithms across a variety of tasks within MiniGrid, DeepMind Control Suite, and Meta-World benchmarks…”
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A nonparametrically corrected likelihood for Bayesian spectral analysis of multivariate time series par Liu, Yixuan, Kirch, Claudia, Lee, Jeong Eun, Meyer, Renate

Table des matières: “… series representation of this prior is then used to develop a Markov chain Monte Carlo algorithm…”
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JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods par Choudhary, Kamal, Wines, Daniel, Li, Kangming, Garrity, Kevin F., Gupta, Vishu, Romero, Aldo H., Krogel, Jaron T., Saritas, Kayahan, Fuhr, Addis, Ganesh, Panchapakesan, Kent, Paul R. C., Yan, Keqiang, Lin, Yuchao, Ji, Shuiwang, Blaiszik, Ben, Reiser, Patrick, Friederich, Pascal, Agrawal, Ankit, Tiwary, Pratyush, Beyerle, Eric, Minch, Peter, Rhone, Trevor David, Takeuchi, Ichiro, Wexler, Robert B., Mannodi-Kanakkithodi, Arun, Ertekin, Elif, Mishra, Avanish, Mathew, Nithin, Baird, Sterling G., Wood, Mitchell, Rohskopf, Andrew Dale, Hattrick-Simpers, Jason, Wang, Shih-Han, Achenie, Luke E. K., Xin, Hongliang, Williams, Maureen, Biacchi, Adam J., Tavazza, Francesca

Table des matières: “… in the form of dataset, code, and meta-data submissions. We cover the following materials design categories…”
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The NANOGrav 15-year Gravitational-Wave Background Methods par Johnson, Aaron D., Meyers, Patrick M., Baker, Paul T., Cornish, Neil J., Hazboun, Jeffrey S., Littenberg, Tyson B., Romano, Joseph D., Taylor, Stephen R., Vallisneri, Michele, Vigeland, Sarah J., Olum, Ken D., Siemens, Xavier, Ellis, Justin A., van Haasteren, Rutger, Hourihane, Sophie, Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Bécsy, Bence, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Chatziioannou, Katerina, Cohen, Tyler, Cordes, James M., Crawford, Fronefield, Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Dolch, Timothy, Drachler, Brendan, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Garver-Daniels, Nate, Gentile, Peter A., Glaser, Joseph, Good, Deborah C., Gültekin, Kayhan, Jennings, Ross J., Jones, Megan L., Kaiser, Andrew R., Kaplan, David L., Kelley, Luke Zoltan, Kerr, Matthew, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Lorimer, Duncan R., Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., McMann, Natasha, Meyers, Bradley W., Mingarelli, Chiara M. F., Mitridate, Andrea, Ng, Cherry, Nice, David J., Ocker, Stella Koch, Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Sardesai, Shashwat C., Schmiedekamp, Carl, Schmiedekamp, Ann, Schmitz, Kai, Shapiro-Albert, Brent J., Simon, Joseph, Siwek, Magdalena S., Stairs, Ingrid H., Stinebring, Daniel R., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Turner, Jacob E., Unal, Caner, Wahl, Haley M., Witt, Caitlin A., Young, Olivia

Table des matières: “…-wave background analyses and their corresponding code representations. Descriptions of the internal…”
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STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction par Luo, Xunlian, Zhu, Chunjiang, Zhang, Detian, Li, Qing

Table des matières: “… provide a systematic review of graph learning strategies and commonly used graph convolution algorithms…”
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A Strong Baseline for Point Cloud Registration via Direct Superpoints Matching par Gupta, Aniket, Xie, Yiming, Singh, Hanumant, Jiang, Huaizu

Table des matières: “… as the first step, e.g., using the Sinkhorn algorithm. A RANSAC-like method is then usually adopted as a post…”
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MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets par Du, Siyi, Bayasi, Nourhan, Hamarneh, Ghassan, Garbi, Rafeef

Table des matières: “… outperforms state-of-the-art algorithms, with superior segmentation performance and a fixed model size…”
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Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception par Liu, Yuntao, Huang, Qian, Li, Rongpeng, Chen, Xianfu, Zhao, Zhifeng, Zhao, Shuyuan, Zhu, Yongdong, Zhang, Honggang

Table des matières: “… algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short…”
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Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks par Ding, Xin, Wang, Yongwei, Xu, Zuheng

Table des matières: “… objective alongside a modified CcGAN training algorithm. Empirical analysis on UTKFace and Steering Angle…”
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Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition par Liu, Hongda, Wang, Yunlong, Ren, Min, Hu, Junxing, Luo, Zhengquan, Hou, Guangqi, Sun, Zhenan

Table des matières: “… algorithms declines sharply when training data suffers from heavy class imbalance. The imbalanced data…”
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Augmenting Chest X-ray Datasets with Non-Expert Annotations par Cheplygina, Veronika, Damgaard, Cathrine, Eriksen, Trine Naja, Juodelyte, Dovile, Jiménez-Sánchez, Amelia

Table des matières: “…The advancement of machine learning algorithms in medical image analysis requires the expansion…”
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CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process par Kontente, Sapir, Orfaig, Roy, Bobrovsky, Ben-Zion

Table des matières: “… navigation. Modern algorithms rely on anchor-based detectors, which are then followed by a label-assignment…”
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Multi-Resolution Active Learning of Fourier Neural Operators par Li, Shibo, Yu, Xin, Xing, Wei, Kirby, Mike, Narayan, Akil, Zhe, Shandian

Table des matières: “… multi-resolution FNO and use ensemble Monte-Carlo to develop an effective posterior inference algorithm…”
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Light Schrödinger Bridge par Korotin, Alexander, Gushchin, Nikita, Burnaev, Evgeny

Table des matières: “… algorithm in discrete optimal transport. We address this issue and propose a novel fast and simple SB solver…”
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