Felix Petersen

Postdoc@Stanford University·美国·OpenReview

研究方向

logic gate networks · fpga · asic · differentiable algorithms · continuous relaxations · neural networks · deep learning · computer graphics · computer vision · inverse problems · unsupervised learning · mathematical information retrieval · mathematical language · language processing

5.0

Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation

ICLR 2025Rejected

一作

4.5

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion

ICLR 2025Poster

8.3

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms

ICLR 2024Rejected

一作

5.0

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms

NeurIPS 2024Poster

一作

4.3

Grounding Everything: Emerging Localization Properties in Vision-Language Transformers

合作者 (14)

Aashwin Ananda Mishra

Felix Petersen

Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion

Convolutional Differentiable Logic Gate Networks

TrAct: Making First-layer Pre-Activations Trainable

Uncertainty Quantification via Stable Distribution Propagation

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms

Grounding Everything: Emerging Localization Properties in Vision-Language Transformers