2 citations · 4 across the 10 of their papers we have counts for
11 papers
Beyond Simple Input-Output Assessment Tasks: Leveraging Automated Programming Assessment for Non-Trivial Courses
Artur Jordao
The public visibility of Artificial Intelligence (AI) is growing rapidly, driven by the positive impact of its applications across diverse fields of knowledge. In this new chapter,…
Compressing LLMs with MoP: Mixture of Pruners
Bruno Lopes Yamamoto, Lucas Lauton de Alcantara, Victor Zacarias +7
The high computational demands of Large Language Models (LLMs) motivate methods that reduce parameter count and accelerate inference. In response, model pruning emerges as an effec…
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…
Pruning Everything, Everywhere, All at Once
Gustavo Henrique do Nascimento, Ian Pons, Anna Helena Reali Costa +1
Deep learning stands as the modern paradigm for solving cognitive tasks. However, as the problem complexity increases, models grow deeper and computationally prohibitive, hindering…
Improving Fairness in LLMs Through Testing-Time Adversaries
Isabela Pereira Gregio, Ian Pons, Anna Helena Reali Costa +1
Large Language Models (LLMs) push the bound-aries in natural language processing and generative AI, driving progress across various aspects of modern society. Unfortunately, the pe…
Efficient LLMs with AMP: Attention Heads and MLP Pruning
Leandro Giusti Mugnaini, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara +5
Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems. In particular, Large Language Models (LLMs) have significantly ad…
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…