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Arpit Bansal
Publications - 11
Citations - 245
Arpit Bansal is an academic researcher. The author has contributed to research in topics: Computer science & k-means clustering. The author has an hindex of 1, co-authored 1 publications receiving 43 citations.
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Journal ArticleDOI
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Arpit Bansal,Eitan Borgnia,Hong-Min Chu,Jie Li,Hamideh Kazemi,Furong Huang,Micah Goldblum,Jonas Geiping,Tom Goldstein +8 more
TL;DR: It is observed that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
Journal ArticleDOI
Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining
TL;DR: Improvement in the kmean clustering algorithm will be proposed which can define number of clusters automatically and assign required cluster to un-clustered points and will leads to improvement in accuracy and reduce clustering time by the member assigned to the cluster to predict cancer.
Proceedings ArticleDOI
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
Gowthami Somepalli,Liam Fowl,Arpit Bansal,Ping Yeh-Chiang,Yehuda Dar,Richard G. Baraniuk,Micah Goldblum,Tom Goldstein +7 more
TL;DR: It is seen that decision boundary reproducibility depends strongly on model width, and how these observations re-late to the theory of double descent phenomena in convex models is discussed.
Journal ArticleDOI
Universal Guidance for Diffusion Models
Arpit Bansal,Hong-Min Chu,Avi Schwarzschild,Soumyadip Sengupta,Micah Goldblum,Jonas Geiping,Tom Goldstein +6 more
TL;DR: This article proposed a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components, including segmentation, face recognition, object detection, and classifier signals.
Proceedings ArticleDOI
Transfer Learning with Deep Tabular Models
Roman Levin,Valeriia Cherepanova,Avi Schwarzschild,Arpit Bansal,C. Bayan Bruss,Tom Goldstein,Andrew Gordon Wilson,Micah Goldblum +7 more
TL;DR: This work finds that transfer learning with deep tabular models provides a definitive advantage over gradient boosted decision tree methods and proposes a pseudo-feature method for cases where the upstream and downstream feature sets differ.