08:53

[WACV 2025 ORAL] HEX: Hierarchical Emergence Exploitation in Self Supervised Algorithms

01:10:10

Spring 2022 Lecture 5: Classification

01:16:17

Spring 2024 Lecture 16: CNNs Training (Part 1)

01:10:06

Spring 2024 Lecture 18: AutoEncoders

30:04

Spring 2024 Lecture 16: CNNs Training (Part 2)

01:12:51

2024 Spring Lecture 15: CNN Architecture

01:16:18

Spring 2024 Lecture 14: CNNs

01:01:21

Spring 2022 Lecture 13: Neural Networks

01:13:27

Spring 2024 Lecture 13: Neural Networks

01:16:14

Spring 2024 Lecture 12: Clustering

57:40

Spring 2024 Lecture 11: Clustering

01:14:08

Spring 2024 Lecture 10: Clustering

01:14:34

Spring 2024 Lecture 9: Regression

01:16:19

Spring 2024 Lecture 8: Regression

01:37:08

Spring 2024 Lecture 7: Regression

01:15:56

Spring 2024 Lecture 6: Classification

01:12:12

Fall 2024 Lecture 2: Classification

01:13:31

Spring 2023 Lecture 24: Uncertainty

58:01

Spring 2023 Lecture 26: Weakly-Supervised and Self-Supervised Learning

01:14:19

Spring 2023 Lecture 25: Anomaly Detection

01:04:06

Spring 2024 Lecture 26: Self-Supervised Learning

01:14:12

Spring 2024 Lecture 25: Active Learning

01:07:13

Spring 2024 Lecture 24: Anomaly Detection

01:13:23

Spring 2023 Lecture 23: Active Learning

01:14:19

Spring 2022 Lecture 24: Anomaly Detection

01:11:05

Spring 2023 Lecture 22: Explainability in Neural Networks

01:13:49

Spring 2024 Lecture 23: Explainability Paradigms and Evaluation

01:14:23

Spring 2024 Lecture 22: Explainability in Neural Networks

01:13:19

Spring 2023 Lecture 21: Explainability in Neural Networks

01:16:46

Spring 2024 Lecture 21: Sequence Modeling

01:15:11

Spring 2024 Lecture 4: Classification

01:08:18

Spring 2024 Lecture 19: VAEs

01:12:57

Spring 2024 Lecture 20: Sequential Modeling

01:04:28

Spring 2023 Lecture 17: CNN best practices

48:05

Spring 2023 Lecture 20: RNN LSTM

01:15:02

Spring 2023 Lecture 19: AutoEncoders

01:15:13

Spring 2023 Lecture 11: Clustering GMM

01:06:35

Spring 2023 Lecture 16: CNN Training

01:16:03

Spring 2023 Lecture 15: CNN Architecture

01:16:21

Spring 2023 Lecture 12: Clustering Eval Measures

55:11

Spring 2023 Lecture 7: Linear Regression

01:15:04

Spring 2023 Lecture 6: Classification

01:14:33

Spring 2023 Lecture 9: RegularizedRegression

55:48

Spring 2023 Lecture 10: Clustering K means

01:13:32

Spring 2023 Lecture 8: PolynomialRegression

01:07:04

Spring 2023 Lecture 3: Classification

01:15:11

Spring 2024 Lecture 4: Classification

01:15:56

Spring 2023 Lecture 4: Classification

01:13:52

Spring 2024 Lecture 5: Classification

01:14:36

Fall 2024 Lecture 1: Introduction

01:16:40

Spring 2023 Lecture 2: Classification

01:14:36

Spring 2024 Lecture 1: Intro

16:47

IEEE BigData’24 paper on Human and Automated Prompting in the Segment Anything Model

21:51

[IEEE BigData’24 Best Paper Runner-up] Targeting Negative Flips in AL using Validation Set

02:49:16

ICME'24 Tutorial on Robust Image Understanding: Explainability, Uncertainty, and Intervenability

03:15:32

WACV’24 Tutorial on Robustness at Inference: Explainability, Uncertainty, and Intervenability

01:00:20

Quantization noise: an engineering approach

01:09:21

Digital Image Processing - Image Denoising

59:38

Digital Image Processing - Image Denoising

01:01:12

Attaining Sparsity in Large Language Models: Is It Easy or Hard?