Learning Identity Mappings with Residual Gates. - Pedro H. P. Savarese (CoRR, 2016)

From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets. - Pedro H. P. Savarese; Mayank Kakodkar; Bruno Ribeiro 001 (AAAI, 2018)

Convergence of Gradient Descent on Separable Data. - Mor Shpigel Nacson; Jason D. Lee; Suriya Gunasekar; Pedro Henrique Pamplona Savarese; Nathan Srebro; Daniel Soudry (AISTATS, 2019)

Building a Massive Corpus for Named Entity Recognition Using Free Open Data Sources. - Daniel Specht Menezes; Ruy Milidiú; Pedro Savarese (BRACIS, 2019)

How do infinite width bounded norm networks look in function space? - Pedro Savarese; Itay Evron; Daniel Soudry; Nathan Srebro (COLT, 2019)

Learning Implicitly Recurrent CNNs Through Parameter Sharing. - Pedro Savarese; Michael Maire (ICLR (Poster), 2019)

On the Convergence of AdaBound and its Connection to SGD. - Pedro Savarese (CoRR, 2019)

Kernel and Rich Regimes in Overparametrized Models. - Blake E. Woodworth; Suriya Gunasekar; Jason D. Lee; Edward Moroshko; Pedro Savarese; Itay Golan; Daniel Soudry; Nathan Srebro (COLT, 2020)

Winning the Lottery with Continuous Sparsification. - Pedro Savarese; Hugo Silva 005; Michael Maire (NeurIPS, 2020)

Information-Theoretic Segmentation by Inpainting Error Maximization. - Pedro Savarese; Sunnie S. Y. Kim; Michael Maire; Greg Shakhnarovich; David McAllester (CVPR, 2021)

Domain-Independent Dominance of Adaptive Methods. - Pedro Savarese; David McAllester; Sudarshan Babu; Michael Maire (CVPR, 2021)

Growing Efficient Deep Networks by Structured Continuous Sparsification. - Xin Yuan; Pedro Henrique Pamplona Savarese; Michael Maire (ICLR, 2021)

Meta-Learning via Learning with Distributed Memory. - Sudarshan Babu; Pedro Savarese; Michael Maire (NeurIPS, 2021)

Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise. - Pedro Savarese; Xin Yuan; Yanjing Li; Michael Maire (NeurIPS, 2022)

Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation. - Xin Yuan; Pedro Savarese; Michael Maire (NeurIPS, 2023)

SySMOL: A Hardware-software Co-design Framework for Ultra-Low and Fine-Grained Mixed-Precision Neural Networks. - Cyrus Zhou; Vaughn Richard; Pedro Savarese; Zachary Hassman; Michael Maire; Michael DiBrino; Yanjing Li (CoRR, 2023)

Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators. - Mingyuan Xiang; Xuhan Xie; Pedro Savarese; Xin Yuan; Michael Maire; Yanjing Li (VTS, 2024)

Approaching Deep Learning through the Spectral Dynamics of Weights. - David Yunis; Kumar Kshitij Patel; Samuel Wheeler; Pedro Savarese; Gal Vardi; Karen Livescu; Michael Maire; Matthew R. Walter (CoRR, 2024)