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05/04/2023
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
Tabular data is one of the most commonly used types of data in machine l...
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04/11/2023
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
No free lunch theorems for supervised learning state that no learner can...
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02/07/2023
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
The strength of modern generative models lies in their ability to be con...
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12/13/2022
What do Vision Transformers Learn? A Visual Exploration
Vision transformers (ViTs) are quickly becoming the de-facto architectur...
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12/07/2022
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Cutting-edge diffusion models produce images with high quality and custo...
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10/23/2022
K-SAM: Sharpness-Aware Minimization at the Speed of SGD
Sharpness-Aware Minimization (SAM) has recently emerged as a robust tech...
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08/19/2022
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Standard diffusion models involve an image transform – adding Gaussian n...
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01/29/2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
A central tenet of Federated learning (FL), which trains models without ...
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04/18/2021
The Intrinsic Dimension of Images and Its Impact on Learning
It is widely believed that natural image data exhibits low-dimensional s...
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02/26/2021
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors
Data poisoning is a threat model in which a malicious actor tampers with...
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02/22/2021
Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems
Deep neural networks are powerful machines for visual pattern recognitio...
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11/24/2020
Analyzing the Machine Learning Conference Review Process
Mainstream machine learning conferences have seen a dramatic increase in...
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06/22/2020
