Linear Weight Interpolation Leads to Transient Performance Gains
Gaurav Iyer
Gintare Karolina Dziugaite
David Rolnick
2024-09-28
TMLR (accepted)
openreview.net
openreview.net
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi
Dhananjay Bhaskar
Erica Lindsey Busch
Laurent Caplette
Rahul Singh
Guillaume Lajoie
Nicholas B Turk-Browne
Smita Krishnaswamy
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (see more)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.
2024-09-27
ArXiv (preprint)
doi.org
arxiv.org
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi
Dhananjay Bhaskar
Erica L. Busch
Laurent Caplette
Rahul Singh
Guillaume Lajoie
Nicholas B. Turk-Browne
Smita Krishnaswamy
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (see more)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.
2024-09-27
ArXiv (preprint)
doi.org
arxiv.org
Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks
Adel Guitouni
Nabil Belacel
Loubna Benabbou
Belaid Moa
Munire Erman
Halim Abdul
2024-09-27
Scientific Reports (published)
doi.org
CALE: Continuous Arcade Learning Environment
Jesse Farebrother
Pablo Samuel Castro
We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare … (see more)et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such as PPO [Schulman et al., 2017] and SAC [Haarnoja et al., 2018]) and value-based agents (such as DQN [Mnih et al., 2015] and Rainbow [Hessel et al., 2018]) on the same environment suite. We provide a series of open questions and research directions that CALE enables, as well as initial baseline results using Soft Actor-Critic. CALE is available as part of the ALE athttps://github.com/Farama-Foundation/Arcade-Learning-Environment.
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David LE MEUR
David Orlando Romero Mogrovejo
Chenyang Lyu
Haryo Akbarianto Wibowo
Teresa Lynn
Injy Hamed
Aditya Nanda Kishore Khandavally
Aishik Mandal
Alina Dragonetti
Artem Abzaliev
Atnafu Lambebo Tonja
Bontu Fufa Balcha
Chenxi Whitehouse
Christian Salamea-Palacios
Dan John Velasco
David Ifeoluwa Adelani
D. Meur
Emilio Villa Cueva
Fajri Koto
Fauzan Farooqui … (see 57 more)
Frederico Belcavello
Ganzorig Batnasan
Gisela Vallejo
Gráinne Caulfield
Guido Ivetta
Haiyue Song
Henok Biadglign Ademtew
Hernán Maina
Holy Lovenia
Israel Abebe Azime
Jan Christian Blaise Cruz
Jay Gala
Jiahui Geng
Jesus-German Ortiz-Barajas
Jinheon Baek
Jocelyn Dunstan
Laura Alonso Alemany
Teresa Clifford
Kumaranage Ravindu Yasas Nagasinghe
Luciana Benotti
Luis Fernando D'Haro
Marcelo Viridiano
Marcos Estecha-Garitagoitia
Maria Camila Buitrago Cabrera
Mario Rodríguez-Cantelar
Mélanie Jouitteau
Mihail Minkov Mihaylov
Mohamed Fazli Mohamed Imam
Muhammad Farid Adilazuarda
Munkhjargal Gochoo
Munkh-Erdene Otgonbold
Naome Etori
Olivier NIYOMUGISHA
Paula Mónica Silva
Pranjal A Chitale
Raj Dabre
Rendi Chevi
Ruochen Zhang
Ryandito Diandaru
Samuel Cahyawijaya
Santiago Góngora
Soyeong Jeong
Sukannya Purkayastha
Tatsuki Kuribayashi
Thanmay Jayakumar
Tiago Timponi Torrent
Toqeer Ehsan
Vladimir Araujo
Yova Kementchedjhieva
Zara Burzo
Zheng Wei Lim
Zheng Xin Yong
Oana Ignat
Joan Nwatu
Rada Mihalcea
Thamar Solorio
Alham Fikri Aji
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (oral)
doi.org
openreview.net
Development of AI-assisted microscopy frameworks through realistic simulation in pySTED
Anthony Bilodeau
Albert Michaud-Gagnon
Julia Chabbert
Benoit Turcotte
Jörn Heine
Audrey Durand
Flavie Lavoie-Cardinal
The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both the image acquisition… (see more) and analysis phases. Development of artificial intelligence (AI)-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.
2024-09-26
Nature Machine Intelligence (published)
doi.org
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
Charles Guille-Escuret
Pierre-Andre Noel
Ioannis Mitliagkas
David Vazquez
Joao Monteiro
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net
Learning Action and Reasoning-Centric Image Editing from Videos and Simulation
Benno Krojer
Dheeraj Vattikonda
Luis Lara
Varun Jampani
Eva Portelance
Chris Pal
Siva Reddy
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)
openreview.net
openreview.net
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
Bowen Li
Zhaoyu Li
Qiwei Du
Jinqi Luo
Wenshan Wang
Yaqi Xie
Simon Stepputtis
Chen Wang
Katia P. Sycara
Pradeep Kumar Ravikumar
Alexander G. Gray
Xujie Si
Sebastian Scherer
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural n… (see more)etworks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks with complex multi-agent interactions. Furthermore, they are usually constrained by fixed and simplistic logical rules over limited entities, making them far from real-world complexities. To address these crucial gaps, we introduce LogiCity, the first simulator based on customizable first-order logic (FOL) for an urban-like environment with multiple dynamic agents. LogiCity models diverse urban elements using semantic and spatial concepts, such as
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
openreview.net
openreview.net
Low-Rank Adaptation Secretly Imitates Differentially Private SGD
Saber Malekmohammadi
Golnoosh Farnadi
As pre-trained language models grow in size, full fine-tuning their parameters on task adaptation data becomes increasingly impractical. To … (see more)address this challenge, some methods for low-rank adaptation of language models have been proposed, e.g. LoRA, which incorporates trainable low-rank decomposition matrices into only some parameters of the pre-trained model, called adapters. This approach significantly reduces the number of trainable parameters compared to fine-tuning all parameters or adapters. In this work, we look at low-rank adaptation method from the lens of data privacy. We show theoretically that the low-rank adaptation used in LoRA is equivalent to fine-tuning adapters with noisy batch gradients - just like what DPSGD algorithm does. We also quantify the variance of the injected noise as a decreasing function of adaptation rank. By establishing a Berry-Esseen type bound on the total variation distance between the injected noise distribution and a Gaussian noise distribution with the same variance, we show that the dynamics of low-rank adaptation is very close to when DPSGD is performed w.r.t the adapters. Following our theoretical findings and approved by our experimental results, we show that low-rank adaptation provides robustness to membership inference attacks w.r.t the fine-tuning data.
2024-09-26
ArXiv (preprint)
arxiv.org
arxiv.org
Reactzyme: A Benchmark for Enzyme-Reaction Prediction
Chenqing Hua
Bozitao Zhong
Sitao Luan
Liang Hong
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net