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Found 12 papers, 5 papers with code
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
This DIMAT paradigm presents a new opportunity for the future decentralized learning, enhancing its adaptability to real-world with sparse and light-weight communication and computation.
Robust Concept Erasure Using Task Vectors
Finally, we show that Diverse Inversion enables us to apply a TV edit only to a subset of the model weights, enhancing the erasure capabilities while better maintaining the core functionality of the model.
LoDIP: Low light phase retrieval with deep image prior
Phase retrieval (PR) is a fundamental challenge in scientific imaging, enabling nanoscale techniques like coherent diffractive imaging (CDI).
Distributionally Robust Classification on a Data Budget
To our knowledge, this is the first result showing (near) state-of-the-art distributional robustness on limited data budgets.
Circumventing Concept Erasure Methods For Text-to-Image Generative Models
Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public.
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural representations.
Revisiting Self-Distillation
We first show that even with a highly accurate teacher, self-distillation allows a student to surpass the teacher in all cases.
Transformer with Fourier Integral Attentions
Multi-head attention empowers the recent success of transformers, the state-of-the-art models that have achieved remarkable success in sequence modeling and beyond.
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers.
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification
For the task of face verification, we explore the utility of harnessing auxiliary facial emotion labels to impose explicit geometric constraints on the embedding space when training deep embedding models.
Laplacian Smoothing Gradient Descent
We propose a class of very simple modifications of gradient descent and stochastic gradient descent.
Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for $k$-means Clustering
In this paper, we propose an implicit gradient descent algorithm for the classic $k$-means problem.
