Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free...
Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the...
View ArticleImplicit Compressibility of Overparametrized Neural Networks Trained with...
Neural network compression has been an increasingly important subject, not only due to its practical relevance, but also due to its theoretical implications, as there is an explicit connection between...
View ArticleConstructing Semantics-Aware Adversarial Examples with Probabilistic Perspective
We propose a probabilistic perspective on adversarial examples. This perspective allows us to view geometric restrictions on adversarial examples as distributions, enabling a seamless shift towards...
View ArticleError Bounds for Flow Matching Methods
Score-based generative models are a popular class of generative modelling techniques relying on stochastic differential equations (SDE). From their inception, it was realized that it was also possible...
View ArticleMaking Language Models Better Tool Learners with Execution Feedback
Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their capabilities and...
View ArticleSmaller Language Models are Better Black-box Machine-Generated Text Detectors
With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machine-generated or...
View ArticleSolar Active Region Magnetogram Image Dataset for Studies of Space Weather
In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar...
View ArticleNeural Wave Functions for Superfluids
Understanding superfluidity remains a major goal of condensed matter physics. Here we tackle this challenge utilizing the recently developed Fermionic neural network (FermiNet) wave function Ansatz for...
View ArticleStyleLipSync: Style-based Personalized Lip-sync Video Generation
In this paper, we present StyleLipSync, a style-based personalized lip-sync video generative model that can generate identity-agnostic lip-synchronizing video from arbitrary audio. To generate a video...
View ArticleA multifidelity approach to continual learning for physical systems
We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of...
View ArticleError-mitigated Quantum Approximate Optimization via Learning-based Adaptive...
Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum computing is envisioned as a powerful tool offering potential computational advantages for...
View ArticleEfficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of...
Pre-trained language models have led to a new state-of-the-art in many NLP tasks. However, for topic modeling, statistical generative models such as LDA are still prevalent, which do not easily allow...
View ArticleNetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c)...
View ArticleCorruption-Robust Algorithms with Uncertainty Weighting for Nonlinear...
Despite the significant interest and progress in reinforcement learning (RL) problems with adversarial corruption, current works are either confined to the linear setting or lead to an undesired...
View ArticleSublinear Time Algorithm for Online Weighted Bipartite Matching
Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex...
View ArticleDifferentially Private Graph Learning via Sensitivity-Bounded Personalized...
Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding. However, while data privacy is one of the most...
View ArticleInertial Newton Algorithms Avoiding Strict Saddle Points
We study the asymptotic behavior of second-order algorithms mixing Newton's method and inertial gradient descent in non-convex landscapes. We show that, despite the Newtonian behavior of these methods,...
View ArticleMachine Collaboration
We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of base machines for prediction tasks. Unlike bagging/stacking (a parallel &...
View ArticleSparse NMF with Archetypal Regularization: Computational and Robustness...
We consider the problem of sparse nonnegative matrix factorization (NMF) using archetypal regularization. The goal is to represent a collection of data points as nonnegative linear combinations of a...
View ArticleCreativity of Deep Learning: Conceptualization and Assessment
While the potential of deep learning (DL) for automating simple tasks is already well explored, recent research has started investigating the use of deep learning for creative design, both for complete...
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