2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
Layer-wise LoRA fine-tuning: a similarity metric approach
Keith Ando Ogawa, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara +5
Pre-training Large Language Models (LLMs) on web-scale datasets becomes fundamental for advancing general-purpose AI. In contrast, enhancing their predictive performance on downstr…
Technical Report on Text Dataset Distillation
Keith Ando Ogawa, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara +6
In the vision domain, dataset distillation arises as a technique to condense a large dataset into a smaller synthetic one that exhibits a similar result in the training process. Wh…
The Virtues of Brevity: Avoid Overthinking in Parallel Test-Time Reasoning
Raul Cavalcante Dinardi, Bruno Yamamoto, Anna Helena Reali Costa +1
Reasoning models represent a significant advance in LLM capabilities, particularly for complex reasoning tasks such as mathematics and coding. Previous studies confirm that paralle…
One Period to Rule Them All: Identifying Critical Learning Periods in Deep Networks
Vinicius Yuiti Fukase, Heitor Gama, Barbara Bueno +3
Critical Learning Periods comprehend an important phenomenon involving deep learning, where early epochs play a decisive role in the success of many training recipes, such as data…
Layer Pruning with Consensus: A Triple-Win Solution
Leandro Giusti Mugnaini, Carolina Tavares Duarte, Anna H. Reali Costa +1
Layer pruning offers a promising alternative to standard structured pruning, effectively reducing computational costs, latency, and memory footprint. While notable layer-pruning ap…
Effective Layer Pruning Through Similarity Metric Perspective
Ian Pons, Bruno Yamamoto, Anna H. Reali Costa +1
Deep neural networks have been the predominant paradigm in machine learning for solving cognitive tasks. Such models, however, are restricted by a high computational overhead, limi…