Risultati della ricerca - decoding ((algorithm OR Algorithmssic) OR (algorithmics OR algorithmics))

Provably Secure Disambiguating Neural Linguistic Steganography di Qi, Yuang, Chen, Kejiang, Zeng, Kai, Zhang, Weiming, Yu, Nenghai

Sommario: “... ambiguity problem, which arises when using language models based on subwords, leads to occasional decoding...”
Testo

CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP di Evuru, Chandra Kiran Reddy, Ghosh, Sreyan, Kumar, Sonal, S, Ramaneswaran, Tyagi, Utkarsh, Manocha, Dinesh

Sommario: “... act as highly effective augmentations, and CoDa can achieve this without intricate decoding-time...”
Testo

TSOM: Small Object Motion Detection Neural Network Inspired by Avian Visual Circuit di Hu, Pignge, Zhang, Xiaoteng, Li, Mengmeng, Zhu, Yingjie, Shi, Li

Sommario: “... to be done on small object motion detection algorithms based on the avian visual system. In this paper, we...”
Testo

Dual-Scale Transformer for Large-Scale Single-Pixel Imaging di Qu, Gang, Wang, Ping, Yuan, Xin

Sommario: “... shrinkagethresholding algorithm (ISTA) into two alternative modules: efficient tensor gradient descent and hybrid...”
Testo

UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach di Ghadaksaz, MohammadMahdi, Mahmood, Mobeen, Le-Ngoc, Tho

Sommario: “... by incorporating an unmanned aerial vehicle (UAV) as a decode-and-forward (DF) relay between the base station (BS...”
Testo

Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models di Zhao, Siyan, Israel, Daniel, Broeck, Guy Van den, Grover, Aditya

Sommario: “... input prompt lengths, prefilling will incur a significant overhead on decoding time. In this work, we...”
Testo

DAGER: Exact Gradient Inversion for Large Language Models di Petrov, Ivo, Dimitrov, Dimitar I., Baader, Maximilian, Müller, Mark Niklas, Vechev, Martin

Sommario: “..., the first algorithm to recover whole batches of input text exactly. DAGER leverages the low-rank structure...”
Testo

Offline Regularised Reinforcement Learning for Large Language Models Alignment di Richemond, Pierre Harvey, Tang, Yunhao, Guo, Daniel, Calandriello, Daniele, Azar, Mohammad Gheshlaghi, Rafailov, Rafael, Pires, Bernardo Avila, Tarassov, Eugene, Spangher, Lucas, Ellsworth, Will, Severyn, Aliaksei, Mallinson, Jonathan, Shani, Lior, Shamir, Gil, Joshi, Rishabh, Liu, Tianqi, Munos, Remi, Piot, Bilal

Sommario: “... algorithms that do not require pairwise preferences. DRO uses a simple mean-squared objective that can...”
Testo

LiSD: An Efficient Multi-Task Learning Framework for LiDAR Segmentation and Detection di Xu, Jiahua, Zuo, Si, Wei, Chenfeng, Zhou, Wei

Sommario: “... remarkable performance gains in comparison to conventional algorithms. However, the segmentation...”
Testo

Towards Continuous Reuse of Graph Models via Holistic Memory Diversification di Qiao, Ziyue, Xiao, Junren, Sun, Qingqiang, Xiao, Meng, Luo, Xiao, Xiong, Hui

Sommario: “... an efficient greedy algorithm for sampling representative training nodes from graphs into memory buffers after...”
Testo

Convex-hull Estimation using XPSNR for Versatile Video Coding di Menon, Vignesh V, Helmrich, Christian R., Wieckowski, Adam, Bross, Benjamin, Marpe, Detlev

Sommario: “... by an encoding time reduction of 44.43% and a decoding time reduction of 65.46%. This shift towards XPSNR as a...”
Testo

Polar-Coded Tensor-Based Unsourced Random Access with Soft Decoding di Fang, Jiaqi, Liang, Yan, Sun, Gangle, Hou, Hongwei, Wang, Yafei, You, Li, Wang, Wenjin

Sommario: “... numer of active devices. However, current TURA scheme lacks the soft decoder, thus precluding...”
Testo

CPT: Consistent Proxy Tuning for Black-box Optimization di He, Yuanyang, Huang, Zitong, Xu, Xinxing, Goh, Rick Siow Mong, Khan, Salman, Zuo, Wangmeng, Liu, Yong, Feng, Chun-Mei

Sommario: “... only as a decoding-time algorithm, leading to an inconsistency between training and testing which...”
Testo

Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints di Wu, Yuxuan, Wang, Ziyu, Raj, Bhiksha, Xia, Gus

Sommario: “... representations from sequences of observations. Unlike most disentanglement algorithms that rely on domain...”
Testo

On the Efficacy of Surface Codes in Compensating for Radiation Events in Superconducting Devices di Vallero, Marzio, Casagranda, Gioele, Vella, Flavio, Rech, Paolo

Sommario: “... of technological advancements to the qubit's stability is saturating, algorithmic solutions, such as quantum error...”
Testo

Neural-based Video Compression on Solar Dynamics Observatory Images di Khoshkhahtinat, Atefeh, Zafari, Ali, Mehta, Piyush M., Nasrabadi, Nasser M., Thompson, Barbara J., Kirk, Michael S. F., da Silva, Daniel

Sommario: “... of the entropy decoding step. The entropy model leverages a channel-dependent approach and utilizes checkerboard...”
Testo

Integrated Intention Prediction and Decision-Making with Spectrum Attention Net and Proximal Policy Optimization di Zhou, Xiao, Meng, Chengzhen, Liu, Wenru, Peng, Zengqi, Liu, Ming, Ma, Jun

Sommario: “... as the predicted intentions are not decoded to trajectories in the executing process. Furthermore, the proximal...”
Testo

MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling di Edwards, Drew, Riley, Xavier, Sarmento, Pedro, Dixon, Simon

Sommario: “... learning solution to symbolic guitar tablature estimation. We train an encoder-decoder Transformer model...”
Testo

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach di Li, Zhiwei, Long, Guodong, Zhou, Tianyi, Jiang, Jing, Zhang, Chengqi

Sommario: “... distributed Collaborative Filtering (CF) algorithms with privacy-preserving mechanisms, and then preserve...”
Testo

An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing di Yue, Xinlang, Liu, Yiran, Shi, Fangzhou, Luo, Sihong, Zhong, Chen, Lu, Min, Xu, Zhe

Sommario: “... solutions mainly follow a two-stage pattern that incorporate heuristic or learning-based algorithms...”
Testo