613 citations · 1.5k across the 47 of their papers we have counts for
15 papers · 1 filter
Do Deep Neural Network Solutions Form a Star Domain?
Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad +1
It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al.,…
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty qua…
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Martin Gubri, Dennis Ulmer, Hwaran Lee +2
Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. Assessing the compliance of the released LLMs is crucial,…
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles
Luca Scimeca, Alexander Rubinstein, Damien Teney +2
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut learning, where a model relies on erroneous…
Trustworthy Machine Learning
Bálint Mucsányi, Michael Kirchhof, Elisa Nguyen +2
As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalize to small changes in the distributi…
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh +1
Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising…
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
Elisa Nguyen, Minjoon Seo, Seong Joon Oh
Training data attribution (TDA) techniques find influential training data for the model's prediction on the test data of interest. They approximate the impact of down- or up-weight…
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh
Contrastively trained encoders have recently been proven to invert the data-generating process: they encode each input, e.g., an image, into the true latent vector that generated t…
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
Damien Teney, Yong Lin, Seong Joon Oh +1
Several studies have compared the in-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP. They report a frequent positive correlation a…
Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh +5
The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condens…
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Luca Scimeca, Seong Joon Oh, Sanghyuk Chun +2
Deep neural networks (DNNs) often rely on easy-to-learn discriminatory features, or cues, that are not necessarily essential to the problem at hand. For example, ducks in an image…
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Michael Poli, Stefano Massaroli, Luca Scimeca +6
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes. Stochastic hybrid systems (SHSs…
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Byeongho Heo, Sanghyuk Chun, Seong Joon Oh +5
Normalization techniques are a boon for modern deep learning. They let weights converge more quickly with often better generalization performances. It has been argued that the norm…
Modeling Uncertainty with Hedged Instance Embedding
Seong Joon Oh, Kevin Murphy, Jiyan Pan +3
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learnin…
Sequential Attacks on Agents for Long-Term Adversarial Goals
Edgar Tretschk, Seong Joon Oh, Mario Fritz
Reinforcement learning (RL) has advanced greatly in the past few years with the employment of effective deep neural networks (DNNs) on the policy networks. With the great effective…