Zobrazeno 1 - 10
of 9 202
pro vyhledávání: '"A. Hadji"'
Autor:
Pardo, Fernando De Meer, Lehmann, Claude, Gehrig, Dennis, Nagy, Andrea, Nicoli, Stefano, Misheva, Branka Hadji, Braschler, Martin, Stockinger, Kurt
In this paper, we present an end-to-end multi-source Entity Matching problem, which we call entity group matching, where the goal is to assign to the same group, records originating from multiple data sources but representing the same real-world enti
Externí odkaz:
http://arxiv.org/abs/2406.15015
Pre-trained Language Models (LMs) exhibit strong zero-shot and in-context learning capabilities; however, their behaviors are often difficult to control. By utilizing Reinforcement Learning from Human Feedback (RLHF), it is possible to fine-tune unsu
Externí odkaz:
http://arxiv.org/abs/2405.20053
Autor:
Paterson, Jessy, Mitra, Sunanda, Liu, Yanqing, Boukhari, Mustapha, Singhal, Dhruv, Lacroix, David, Hadji, Emmanuel, Barski, André, Tainoff, Dimitri, Bourgeois, Olivier
Nano-engineering crystalline materials can be used to tailor their thermal properties. By adding new nanoscale phonon scattering centers and controlling their size, one can effectively decrease the phonon mean free path and hence the thermal conducti
Externí odkaz:
http://arxiv.org/abs/2404.19550
Content and image generation consist in creating or generating data from noisy information by extracting specific features such as texture, edges, and other thin image structures. We are interested here in generative models, and two main problems are
Externí odkaz:
http://arxiv.org/abs/2403.14897
Autor:
Osterrieder, Joerg, Chan, Stephen, Chu, Jeffrey, Zhang, Yuanyuan, Misheva, Branka Hadji, Mare, Codruta
Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity
Externí odkaz:
http://arxiv.org/abs/2402.11231
In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image super-resolution that yields state-of-the-art results using only a single DDIM step. We propose a novel scale distillation approach to train our SR model. Instead
Externí odkaz:
http://arxiv.org/abs/2401.17258
Autor:
Pham, Hai X., Hadji, Isma, Xu, Xinnuo, Degutyte, Ziedune, Rainey, Jay, Kazakos, Evangelos, Fazly, Afsaneh, Tzimiropoulos, Georgios, Martinez, Brais
In this paper, we focus on task-specific question answering (QA). To this end, we introduce a method for generating exhaustive and high-quality training data, which allows us to train compact (e.g., run on a mobile device), task-specific QA models th
Externí odkaz:
http://arxiv.org/abs/2401.13594
We consider the accuracy of an approximate posterior distribution in nonparametric regression problems by combining posterior distributions computed on subsets of the data defined by the locations of the independent variables. We show that this appro
Externí odkaz:
http://arxiv.org/abs/2312.14130
This paper introduces a novel Parameter-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models. PEFT techniques such as LoRA, BitFit and IA3 have demonstrated comparable performance to fu
Externí odkaz:
http://arxiv.org/abs/2312.08900
Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks, offering advantages such as enhanced customer experience, democratising financial services, improving consumer protection, and enhanc
Externí odkaz:
http://arxiv.org/abs/2311.07513