Media Summary: 02 - Bommireddi - Testing submodularity and other properties of valuation functions We are still in actually part one of the of the lecture and I think that from my my A 0.9 billion parameter model scored 96.33% on OmniDocBench v1.6. A 235 billion parameter model scored 89.78%. The smaller ...

02 Bommireddi Testing Submodularity And - Detailed Analysis & Overview

02 - Bommireddi - Testing submodularity and other properties of valuation functions We are still in actually part one of the of the lecture and I think that from my my A 0.9 billion parameter model scored 96.33% on OmniDocBench v1.6. A 235 billion parameter model scored 89.78%. The smaller ... In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes ... Stefanie Jegelka, MIT Foundations of Machine ... In this lecture we give the basic greedy algorithm, and give the proof by Wolsey, Nemhauser and Fisher stating that if \mathcal{I} is ...

Spotlight video for paper "Adaptive Maximization of Pointwise The Fujishige-Wolfe heuristic is empirically one of the fastest algorithms for

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02 - Bommireddi - Testing submodularity and other properties of valuation functions
Learning and testing submodular functions
Submodularity and Optimization -- Jeff Bilmes (Part 2)
A 0.9B Model Just Beat 235B: The AI Scaling Law Has a Breaking Point
Oral Session: Sampling from Probabilistic Submodular Models
ISIT 2018 - Jeffrey Bilmes and Amin Karbasi - Submodularity in Information and Data Science
Interactive Learning of Mixtures of Submodular Functions
Submodularity and Optimization -- Jeff Bilmes (Part 1)
Robust Sensor Placements and Submodular Functions
Submodularity: Theory and Applications I
10.2 Submodular Functions, Part II
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint (NIPS 2016)
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02 - Bommireddi - Testing submodularity and other properties of valuation functions

02 - Bommireddi - Testing submodularity and other properties of valuation functions

02 - Bommireddi - Testing submodularity and other properties of valuation functions

Learning and testing submodular functions

Learning and testing submodular functions

Submodular

Submodularity and Optimization -- Jeff Bilmes (Part 2)

Submodularity and Optimization -- Jeff Bilmes (Part 2)

We are still in actually part one of the of the lecture and I think that from my my

A 0.9B Model Just Beat 235B: The AI Scaling Law Has a Breaking Point

A 0.9B Model Just Beat 235B: The AI Scaling Law Has a Breaking Point

A 0.9 billion parameter model scored 96.33% on OmniDocBench v1.6. A 235 billion parameter model scored 89.78%. The smaller ...

Oral Session: Sampling from Probabilistic Submodular Models

Oral Session: Sampling from Probabilistic Submodular Models

Submodular and

ISIT 2018 - Jeffrey Bilmes and Amin Karbasi - Submodularity in Information and Data Science

ISIT 2018 - Jeffrey Bilmes and Amin Karbasi - Submodularity in Information and Data Science

2018 ISIT Tutorial Vail, CO - 6/17/18

Interactive Learning of Mixtures of Submodular Functions

Interactive Learning of Mixtures of Submodular Functions

Jeff Bilmes, University of Washington https://simons.berkeley.edu/talks/jeff-bilmes-

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Intro ...

Robust Sensor Placements and Submodular Functions

Robust Sensor Placements and Submodular Functions

In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes ...

Submodularity: Theory and Applications I

Submodularity: Theory and Applications I

Stefanie Jegelka, MIT https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-1 Foundations of Machine ...

10.2 Submodular Functions, Part II

10.2 Submodular Functions, Part II

In this lecture we give the basic greedy algorithm, and give the proof by Wolsey, Nemhauser and Fisher stating that if \mathcal{I} is ...

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint (NIPS 2016)

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint (NIPS 2016)

Spotlight video for paper "Adaptive Maximization of Pointwise

Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm

Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm

The Fujishige-Wolfe heuristic is empirically one of the fastest algorithms for