Using neural biomarkers to personalize dosing of vagus nerve stimulation
Antonin Berthon
Lorenz Wernisch
Myrta Stoukidi
Michael Thornton
Olivier Tessier-Lariviere
Pascal Fortier-Poisson
Jorin Mamen
Max Pinkney
Susannah Lee
Elvijs Sarkans
Luca Annecchino
Ben Appleton
Philip Garsed
Bret Patterson
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Tristan Edwards
Aleksi Tukiainen
Joel Jennings … (see 3 more)
Emil Hewage
Oliver Armitage
Cell Morphology-Guided Small Molecule Generation with GFlowNets
Stephen Zhewen Lu
Ziqing Lu
Ehsan Hajiramezanali
Tommaso Biancalani
Gabriele Scalia
Michał Koziarski
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding
Le Zhang
Rabiul Awal
Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream … (see more)tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in"bag-of-words"representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks. We open-source our code at https://github.com/lezhang7/Enhance-FineGrained.
Expressivity of Neural Networks with Fixed Weights and Learned Biases
Ezekiel Williams
Avery Hee-Woon Ryoo
Thomas Jiralerspong
Alexandre Payeur
Luca Mazzucato
Gradient descent induces alignment between weights and the pre-activation tangents for deep non-linear networks
Daniel Beaglehole
Atish Agarwala
Understanding the mechanisms through which neural networks extract statistics from input-label pairs is one of the most important unsolved p… (see more)roblems in supervised learning. Prior works have identified that the gram matrices of the weights in trained neural networks of general architectures are proportional to the average gradient outer product of the model, in a statement known as the Neural Feature Ansatz (NFA). However, the reason these quantities become correlated during training is poorly understood. In this work, we clarify the nature of this correlation and explain its emergence at early training times. We identify that the NFA is equivalent to alignment between the left singular structure of the weight matrices and the newly defined pre-activation tangent kernel. We identify a centering of the NFA that isolates this alignment and is robust to initialization scale. We show that, through this centering, the speed of NFA development can be predicted analytically in terms of simple statistics of the inputs and labels.
Gradient descent induces alignment between weights and the pre-activation tangents for deep non-linear networks
Daniel Beaglehole
Atish Agarwala
Understanding the mechanisms through which neural networks extract statistics from input-label pairs is one of the most important unsolved p… (see more)roblems in supervised learning. Prior works have identified that the gram matrices of the weights in trained neural networks of general architectures are proportional to the average gradient outer product of the model, in a statement known as the Neural Feature Ansatz (NFA). However, the reason these quantities become correlated during training is poorly understood. In this work, we clarify the nature of this correlation and explain its emergence at early training times. We identify that the NFA is equivalent to alignment between the left singular structure of the weight matrices and the newly defined pre-activation tangent kernel. We identify a centering of the NFA that isolates this alignment and is robust to initialization scale. We show that, through this centering, the speed of NFA development can be predicted analytically in terms of simple statistics of the inputs and labels.
Gradient Dissent in Language Model Training and Saturation
Andrei Mircea
Ekaterina Lobacheva
We seek to shed light on language model (LM) saturation from the perspective of learning dynamics. To this end, we define a decomposition o… (see more)f the cross-entropy gradient, which forms a shared low-dimensional basis for analyzing the training dynamics of models across scales. Intuitively, this decomposition consists of attractive and repulsive components that increase the logit of the correct class and decrease the logits of incorrect classes, respectively. Our analysis in this subspace reveals a phenomenon we term \textit{gradient dissent}, characterized by gradient components becoming systematically opposed such that loss cannot be improved along one component without being degraded along the other. Notably, we find that complete opposition, which we term \textit{total dissent}, reliably occurs in tandem with the saturation of smaller LMs. Based on these results, we hypothesize that gradient dissent can provide a useful foundation for better understanding and mitigating saturation.
Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics
Antoine Bourdin
Ronan Legin
Matthew Ho
Alexandre Adam
Linear Weight Interpolation Leads to Transient Performance Gains
Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks
Xinyu Qian
Amirozhan Dehghani
Asa Borzabadi Farahani
Across the primate cortex, neurons that perform similar functions tend to be spatially grouped together. In high-level visual cortex, this w… (see more)idely observed biological rule manifests itself as a modular organization of neuronal clusters, each tuned to a specific object category. The tendency toward short connections is one of the most widely accepted views of why such an organization exists in the brains of many animals. Yet, how such a feat is implemented at the neural level remains unclear. Here, using artificial deep neural networks as test beds, we demonstrate that a topographical organization similar to that in the primary, intermediate, and high-level human visual cortex emerges when units in these models are laterally connected and their weight parameters are tuned by top-down credit assignment. Importantly, the emergence of the modular organization in the absence of explicit topography-inducing learning rules and objectives questions their necessity and suggests that local lateral connectivity alone may be sufficient for the formation of the topographic organization across the cortex.
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Oren Kraus
Kian Kenyon-Dean
Saber Saberian
Maryam Fallah
Peter McLean
Jess Leung
Vasudev Sharma
Ayla Khan
Jia Balakrishnan
Safiye Celik
Maciej Sypetkowski
Chi Vicky Cheng
Kristen Morse
Maureen Makes
Ben Mabey
Berton Earnshaw
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek
Florian Bordes
Pietro Astolfi
Mary Williamson
Vasu Sharma