Predicting College Enrollment for Low-Socioeconomic-Status Students Using Machine Learning Approaches
Surina He
Mehrdad Yousefpoori-Naeim
Ying Cui
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam
Munyeong Kim
Frida-Cecilia Acosta-Parenteau
Daniel Lee
Amine Mhedhbi
Elena L. Glassman
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam
Munyeong Kim
Frida-Cecilia Acosta-Parenteau
Daniel Lee
Amine Mhedhbi
Elena L. Glassman
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus
Maksim Kuznetsov
Roman Schutski
Shayakhmetov Rim
Daniil Polykovskiy
Alex Zhavoronkov
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding … (voir plus)of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample.
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
Nikolaos Ioannis Bountos
Arthur Ouaknine
Ioannis Papoutsis
Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG
Adrien Dubois
Elisabeth Audet-Duchesne
Inga Sophia Knoth
Charles-Olivier Martin
Khadije Jizi
Petra Tamer
Nadine Younis
Sébastien Jacquemont
Sarah Lippé
A Layer Selection Approach to Test Time Adaptation
Sabyasachi Sahoo
Mostafa ElAraby
Jonas Ngnawe
Yann Batiste Pequignot
Frederic Precioso
Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (voir plus) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.
StarVector: Generating Scalable Vector Graphics Code from Images and Text
Juan A. Rodriguez
Abhay Puri
Shubham Agarwal
Issam Hadj Laradji
Pau Rodriguez
Sai Rajeswar
David Vazquez
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Amirhossein Abaskohi
Amrutha Varshini Ramesh
Shailesh Nanisetty
Chirag Goel
David Vazquez
Spandana Gella
Giuseppe Carenini
Issam Hadj Laradji
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Amirhossein Abaskohi
Amrutha Varshini Ramesh
Shailesh Nanisetty
Chirag Goel
David Vazquez
Spandana Gella
Giuseppe Carenini
Issam Hadj Laradji
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
Amir Ali Farzin
Yuen-Man Pun
Philipp Braun
Youssef Diouane
Iman Shames
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Saeid Jamshidi
Amin Nikanjam
Nafi Kawser Wazed