Publications

Galileo: Learning Global&Local Features of Many Remote Sensing Modalities

Gabriel Tseng

Anthony Fuller

Marlena Reil

Henry Herzog

Patrick Beukema

Favyen Bastani

James R Green

Evan Shelhamer

Hannah Kerner

David Rolnick

We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, … (see more)elevation, weather, pseudo-labels, and more - across space and time. These inputs are useful for diverse remote sensing tasks, such as crop mapping and flood detection. However, learning shared representations of remote sensing data is challenging, given the diversity of relevant data modalities, and because objects of interest vary massively in scale, from small boats (1-2 pixels and fast) to glaciers (thousands of pixels and slow). We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling. Our dual global and local contrastive losses differ in their targets (deep representations vs. shallow input projections) and masking strategies (structured vs. not). Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.

2025-05-01

ICML.cc/2025/Conference (poster)

proceedings.mlr.press

openreview.net

Generative AI: Hype, Hope, and Responsible Use in Science and Everyday Life

Doina Precup

2025-05-01

Biological Psychiatry (published)

doi.org

Half Search Space is All You Need

Pavel Rumiantsev

Mark Coates

2025-05-01

arXiv (published)

doi.org

arxiv.org

HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts

Neil He

Rishabh Anand

Hiren Madhu

Ali Maatouk

Smita Krishnaswamy

Leandros Tassiulas

Menglin Yang 0001

Rex Ying

2025-05-01

arXiv (published)

doi.org

arxiv.org

Impact of through‐slice gradient optimization for dynamic slice‐wise shimming in the cervico‐thoracic spinal cord

Arnaud Breheret

Alexandre D'Astous

Yixin Ma

Jason P. Stockmann

Julien Cohen-Adad

2025-05-01

Magnetic Resonance in Medicine (published)

doi.org

Improving Multilingual Math Reasoning for African Languages

Odunayo Ogundepo

Akintunde Oladipo

Kelechi Ogueji

Esther Adenuga

David Ifeoluwa Adelani

Jimmy Lin

Researchers working on low-resource languages face persistent challenges due to limited data availability and restricted access to computati… (see more)onal resources. Although most large language models (LLMs) are predominantly trained in high-resource languages, adapting them to low-resource contexts, particularly African languages, requires specialized techniques. Several strategies have emerged for adapting models to low-resource languages in todays LLM landscape, defined by multi-stage pre-training and post-training paradigms. However, the most effective approaches remain uncertain. This work systematically investigates which adaptation strategies yield the best performance when extending existing LLMs to African languages. We conduct extensive experiments and ablation studies to evaluate different combinations of data types (translated versus synthetically generated), training stages (pre-training versus post-training), and other model adaptation configurations. Our experiments focuses on mathematical reasoning tasks, using the Llama 3.1 model family as our base model.

2025-05-01

arXiv (published)

doi.org

arxiv.org

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reyhane Askari Hemmat

Mohammad Pezeshki

Elvis Dohmatob

Florian Bordes

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Michal Drozdzal

Adriana Romero Soriano

2025-05-01

ICML.cc/2025/Conference (oral)

doi.org

openreview.net

Learning Penalty for Optimal Partitioning via Automatic Feature Extraction

Tung L. Nguyen

Toby Dylan Hocking

Changepoint detection identifies significant shifts in data sequences, making it important in areas like finance, genetics, and healthcare. … (see more)The Optimal Partitioning algorithms efficiently detect these changes, using a penalty parameter to limit the changepoints number. Determining the appropriate value for this penalty can be challenging. Traditionally, this process involved manually extracting statistical features, such as sequence length or variance to make the prediction. This study proposes a novel approach that uses recurrent neural networks to learn this penalty directly from raw sequences by automatically extracting features. Experiments conducted on 20 benchmark genomic datasets show that this novel method surpasses traditional methods in partitioning accuracy in most cases.

2025-05-01

arXiv (published)

doi.org

arxiv.org

Leveraging Per-Instance Privacy for Machine Unlearning

Nazanin Mohammadi Sepahvand

Anvith Thudi

Berivan Isik

Ashmita Bhattacharyya

Nicolas Papernot

Eleni Triantafillou

Daniel M. Roy

Gintare Karolina Dziugaite

2025-05-01

ICML.cc/2025/Conference (poster)

doi.org

openreview.net

LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs

Foundation models based on large language models (LLMs) have shown great success in handling various tasks and modalities. However, adapting… (see more) these models for general-purpose audio-language tasks is challenging due to differences in acoustic environments and task variations. In this work, we introduce LiSTEN Learning Soft Token Embeddings for Neural Audio LLMs), a framework for adapting LLMs to speech and audio tasks. LiSTEN uses a dynamic prompt selection strategy with learnable key-value pairs, allowing the model to balance general and task-specific knowledge while avoiding overfitting in a multitask setting. Our approach reduces dependence on large-scale ASR or captioning datasets, achieves competitive performance with fewer trainable parameters, and simplifies training by using a single-stage process. Additionally, LiSTEN enhances interpretability by analyzing the diversity and overlap of selected prompts across different tasks.

2025-05-01

arXiv (published)

doi.org

arxiv.org

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Sergio Arnaud

Paul McVay

Ada Martin

Arjun Majumdar

Krishna Murthy

Phillip Thomas

Ruslan Partsey