Leveraging Unlabeled Data for Machine Learning in the Electrocardiogram (James Brundage (UU HSC) )
Utah DataScience
Leveraging Unlabeled Data for Machine Learning in the Electrocardiogram (James Brundage (UU HSC) )
1:21:32
Anchoring Multimodal Narrative Generation (Khyathi Chandu)
Utah DataScience
Anchoring Multimodal Narrative Generation (Khyathi Chandu)
1:04:34
Mathematical Modeling for Landmark and 3D Object Detection (Abhinav Kumar, MSU)
Utah DataScience
Mathematical Modeling for Landmark and 3D Object Detection (Abhinav Kumar, MSU)
52:57
Identifying Keyphrases from Text Documents - From Heuristics to Language Models (Debanjan Mahata)
Utah DataScience
Identifying Keyphrases from Text Documents - From Heuristics to Language Models (Debanjan Mahata)
1:12:01
Quantifying Gerrymandering: Hearing the Will of the People
Utah DataScience
Quantifying Gerrymandering: Hearing the Will of the People
57:56
Neural Networks Expressivity through the lens of Dynamical Systems
Utah DataScience
Neural Networks Expressivity through the lens of Dynamical Systems
1:14:49
Title: The Curious Case of Figurative Language (Tuhin Chakrabarty, Columbia University)
Utah DataScience
Title: The Curious Case of Figurative Language (Tuhin Chakrabarty, Columbia University)
1:10:09
Understanding the Convergence of Optimization Algorithms for Minimax Machine Learning Yi Zhou (UECE)
Utah DataScience
Understanding the Convergence of Optimization Algorithms for Minimax Machine Learning Yi Zhou (UECE)
1:07:50
Towards the Development of Models that Learn New Tasks from Instructions
Utah DataScience
Towards the Development of Models that Learn New Tasks from Instructions
56:37
Designing Neural Networks for Efficient Encrypted Inference ( Chinmay Hedge (NYU) )
Utah DataScience
Designing Neural Networks for Efficient Encrypted Inference ( Chinmay Hedge (NYU) )
1:00:45
Epistemic values in feature importance methods: Lessons from feminist epistemology (Lizzie Kumar)
Utah DataScience
Epistemic values in feature importance methods: Lessons from feminist epistemology (Lizzie Kumar)
56:20
Game Theoretic Statistics (Dr. Arun Sai Suggala, CMU)
Utah DataScience
Game Theoretic Statistics (Dr. Arun Sai Suggala, CMU)
45:07
As We Are: Detecting and Mitigating Human Bias in Visual Analytics (Dr Emily Wall, Emory University)
Utah DataScience
As We Are: Detecting and Mitigating Human Bias in Visual Analytics (Dr Emily Wall, Emory University)
1:03:54
Boundary thickness and robustness in learning models (Yaoqing Yang)
Utah DataScience
Boundary thickness and robustness in learning models (Yaoqing Yang)
1:01:23
Logic based classification for Low Resource Setting (Vivek Gupta, UoU)
Utah DataScience
Logic based classification for Low Resource Setting (Vivek Gupta, UoU)
52:01
Using AI to be a Better Friend to Customers (Alan Whitaker, BambooHR)
Utah DataScience
Using AI to be a Better Friend to Customers (Alan Whitaker, BambooHR)
46:33
An Improved Analysis of the Quadtree for High Dimensional EMD (Rajesh Jayaram, CMU)
Utah DataScience
An Improved Analysis of the Quadtree for High Dimensional EMD (Rajesh Jayaram, CMU)
1:28:48
Distributed Inference under Local Information Constraints (Ziteng Sun from EECS)
Utah DataScience
Distributed Inference under Local Information Constraints (Ziteng Sun from EECS)
1:00:04
A Systematic Understanding of Exempt and Non-Exempt Algorithmic Biases (Sanghamitra Dutta,CMU)
Utah DataScience
A Systematic Understanding of Exempt and Non-Exempt Algorithmic Biases (Sanghamitra Dutta,CMU)
1:01:10
Grad Students Spotlights
Utah DataScience
Grad Students Spotlights
1:09:58
Tight Bounds for Adversarially Robust Streams and Sliding Windows ( Samson Zhou (CMU))
Utah DataScience
Tight Bounds for Adversarially Robust Streams and Sliding Windows ( Samson Zhou (CMU))
1:07:48
Visual Pattern Exploration At and Across Scales (Fritz Lekschas, Harvard)
Utah DataScience
Visual Pattern Exploration At and Across Scales (Fritz Lekschas, Harvard)
1:06:49
Unexpected Effects of Online no-Substitution k-means Clustering (Michal Moshkovitz)
Utah DataScience
Unexpected Effects of Online no-Substitution k-means Clustering (Michal Moshkovitz)
51:21
A Tale of Evidence and Explanations - Danish Pruthi (LTI, CMU)
Utah DataScience
A Tale of Evidence and Explanations - Danish Pruthi (LTI, CMU)
1:06:11
Low-rank Subspaces for Unsupervised Entity Linking - Akhil Arora (EPFL)
Utah DataScience
Low-rank Subspaces for Unsupervised Entity Linking - Akhil Arora (EPFL)
1:16:02
Towards High-Performance Machine Learning on the Edge - Akanksha Atrey (UMass)
Utah DataScience
Towards High-Performance Machine Learning on the Edge - Akanksha Atrey (UMass)
1:08:09
An Information Bottleneck Approach for Rationale Extraction - Bhargavi Paranjape (UWash)
Utah DataScience
An Information Bottleneck Approach for Rationale Extraction - Bhargavi Paranjape (UWash)
1:13:57
Data Visualization: How to Make Good Decisions (Prof. Alberto Cairo)
Utah DataScience
Data Visualization: How to Make Good Decisions (Prof. Alberto Cairo)
1:26:45
Semiparametrics: A Biostatistician’s Toolbox (Prof. Daniel Scharfstein)
Utah DataScience
Semiparametrics: A Biostatistician’s Toolbox (Prof. Daniel Scharfstein)
1:21:58
Global Table Extractor (GTE) - Nancy Wang (IBM Research Almaden)
Utah DataScience
Global Table Extractor (GTE) - Nancy Wang (IBM Research Almaden)
1:00:48
DQI: Measuring Data Quality in NLP - Swaroop Mishra (Arizona State University)
Utah DataScience
DQI: Measuring Data Quality in NLP - Swaroop Mishra (Arizona State University)
49:25
Examining Extra Sentential Abilities of Contextual Embeddings - Varun Gangal (LTI, CMU)
Utah DataScience
Examining Extra Sentential Abilities of Contextual Embeddings - Varun Gangal (LTI, CMU)
1:06:52
Identifying Affective Events and the Reasons for their Polarity - Prof. Ellen Riloff (SoC, UoU)
Utah DataScience
Identifying Affective Events and the Reasons for their Polarity - Prof. Ellen Riloff (SoC, UoU)
1:08:52
Learning to care: Capabilities and opportunities; Future vision - Matthew Samore & Jeffrey Humpherys
Utah DataScience
Learning to care: Capabilities and opportunities; Future vision - Matthew Samore & Jeffrey Humpherys
1:24:11
Optimization methods for imposing Fairness in Computer Vision Models - Vishnu Lokhande (UW-Madison)
Utah DataScience
Optimization methods for imposing Fairness in Computer Vision Models - Vishnu Lokhande (UW-Madison)
1:11:23
Characterizing Asymptotic Performance of Inverse Problems over Deep Networks - Parthe Pandit (UCLA)
Utah DataScience
Characterizing Asymptotic Performance of Inverse Problems over Deep Networks - Parthe Pandit (UCLA)
1:08:08
Contextualized Weak Supervision for Text Classification - Dheeraj Mekala (UCSD)
Utah DataScience
Contextualized Weak Supervision for Text Classification - Dheeraj Mekala (UCSD)
48:15
InfoTabS: Inference on Tables as Semi-Structured Data - Vivek Gupta
Utah DataScience
InfoTabS: Inference on Tables as Semi-Structured Data - Vivek Gupta
1:07:53
Fads, Fallacies and Fantasies in the Name of Machine Learning (Vivek Srikumar, University of Utah)
Utah DataScience
Fads, Fallacies and Fantasies in the Name of Machine Learning (Vivek Srikumar, University of Utah)
1:05:31
A Primer on the Geometry in Machine Learning (Jeff Phillips)
Utah DataScience
A Primer on the Geometry in Machine Learning (Jeff Phillips)
1:01:33
Making sense of Twitter @ Bloomberg (Daniel Preoțiuc-Pietro)
Utah DataScience
Making sense of Twitter @ Bloomberg (Daniel Preoțiuc-Pietro)
1:17:31
Data Driven Art & Visualization (Shirley Wu)
Utah DataScience
Data Driven Art & Visualization (Shirley Wu)
1:07:57
Visual Dialog - Towards Communicative Visual Agents (Dr. Satwik Kottur, Facebook AI Research)
Utah DataScience
Visual Dialog - Towards Communicative Visual Agents (Dr. Satwik Kottur, Facebook AI Research)
56:23
Collaborative Embodied Agents (Unnat Jain, UIUC)
Utah DataScience
Collaborative Embodied Agents (Unnat Jain, UIUC)
51:57
Entity Linking for Low-Resource Languages (Shruti Rijhwani, CMU)
Utah DataScience
Entity Linking for Low-Resource Languages (Shruti Rijhwani, CMU)
56:25
Robust Incident Forecasting and Response (Dr. Ayan Mukhopadhyay, Stanford University)
Utah DataScience
Robust Incident Forecasting and Response (Dr. Ayan Mukhopadhyay, Stanford University)
1:09:15
Graph Representation Learning in the Presence of Outliers (Sambaran, IBM Research & IISC Bangalore)
Utah DataScience
Graph Representation Learning in the Presence of Outliers (Sambaran, IBM Research & IISC Bangalore)
1:01:13
Two-Sided Fairness Guarantees for Recommendation Systems (Arpita Biswas, IISC)
Utah DataScience
Two-Sided Fairness Guarantees for Recommendation Systems (Arpita Biswas, IISC)
1:01:44
Tea: High-level Language and Runtime System for Automating Statistical Analysis (Eunice Jun, UWash)
Utah DataScience
Tea: High-level Language and Runtime System for Automating Statistical Analysis (Eunice Jun, UWash)
1:00:06
Understanding the Training of Depth 2 Nets (Anirbit Mukherjee, John Hopkins U)
Utah DataScience
Understanding the Training of Depth 2 Nets (Anirbit Mukherjee, John Hopkins U)
40:31
Neural Module Networks for Reasoning over Text (Nitish Gupta, UPenn)
Utah DataScience
Neural Module Networks for Reasoning over Text (Nitish Gupta, UPenn)
51:56