Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

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We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning app ...

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Evaluating the visual quality of distorted images is a crucial task in the field of image compression, as artifacts may significantly impair the appeal and fidelity of images, therefore reducing the user experience. As assessing the visual quality of image ...

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Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

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Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Chat with Graph Search

The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games

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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Robust machine learning for neuroscientific inference

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

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Unlabeled Principal Component Analysis and Matrix Completion

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

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Machine Learning Security in Industry: A Quantitative Survey

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Equivariant Neural Architectures for Representing and Generating Graphs

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Interpretable Representation Learning and Evaluation for Abstractive Summarization

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