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#Model Interpretability

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Massachusetts Institute of Technology

MIT Deep Learning in Life Sciences Spring 2020

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Explore deep learning applications in genomics, from neural networks to single-cell analysis. Gain insights into regulatory genomics, epigenomics, and genetics through advanced computational techniques.
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15 Lesons
19 hours
On-Demand
Free-Video
Open Data Science

Beyond Aesthetics: Do Text-to-Image Models Equally Serve Everyone?

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Explore the ethical and social implications of Text-to-Image (T2I) models in this 26-minute conference talk by Nithish Kannen, an AI resident at Google DeepMind. Delve into the cultural disparities these models can inadvertently perpetuate due to their d…
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7 Lesons
26 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

The Privacy Considerations of Production Machine Learning

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Explore state-of-the-art research on privacy in machine learning, including estimating and defending against privacy leakage, secure collaboration, and machine unlearning for GDPR compliance.
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1 Lesons
32 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Machine Learning for Entertainment - Challenges and Opportunities

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Explore challenges and solutions in ML for streaming services, including personalization, content understanding, and ads targeting. Learn about balancing metrics, feedback loops, and algorithmic fairness.
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1 Lesons
33 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

5 Techniques to Increase Adoption Rates via Interpretability

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Discover 5 techniques to boost ML project adoption through enhanced model interpretability, fostering trust and bridging gaps between end-users and data scientists.
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1 Lesons
33 minutes
On-Demand
Free-Video
ANITI Toulouse

FOCUS TECHNIQUE: ANITI Gems AI - Library for Machine Learning Model Explainability

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Découvrez ANITI Gems ai, une librairie innovante pour expliquer les modèles de Machine Learning. Apprenez à interpréter et comprendre les décisions des algorithmes d'IA.
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1 Lesons
56 minutes
On-Demand
Free-Video
1littlecoder

SHAP and Shapley Values for Interpretable Machine Learning - Kaggle 30 Days of ML Day 18

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Explore SHAP and Shapley Values for interpretable machine learning. Learn local interpretation techniques, Python implementation, and visualization methods for explainable AI.
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1 Lesons
21 minutes
On-Demand
Free-Video
VinAI

Parameter Estimation and Interpretability in Bayesian Mixture Models

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Explore posterior contraction in Bayesian mixture modeling, examining parameter estimation and model interpretability under correct and misspecified conditions. Gain insights into tradeoffs between expressiveness and interpretability.
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1 Lesons
1 hour 30 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics

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Explore machine learning for nonlinear dynamical systems modeling, focusing on the SINDy algorithm and its applications in fluid dynamics, with emphasis on interpretable and physics-respecting solutions.
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1 Lesons
58 minutes
On-Demand
Free-Video
OSACon

Making AI Truly Open Source - Challenges and Solutions

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Explore the complexities of truly open-source AI, examining transparency challenges, data attribution issues, and practical solutions for creating genuinely open AI systems.
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1 Lesons
27 minutes
On-Demand
Free-Video
Data Science Conference

Revealing the Unseen: Visualizing Attention in Transformer Models

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Dive into the visualization techniques of attention mechanisms in Transformer models, understanding their inner workings and practical applications through expert insights.
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1 Lesons
24 minutes
On-Demand
Free-Video
Data Science Conference

AI Operations: Evolution, Challenges and Solutions in Modern IT Infrastructure

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Explore the evolution and implementation challenges of AI Ops, from historical Big Data solutions to modern artificial intelligence integration in IT operations management and workflow optimization.
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1 Lesons
24 minutes
On-Demand
Free-Video
UofU Data Science

Pairwise Feature Importance and Effective Attention in Self-Attention Networks

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Dive into advanced concepts of attention mechanisms, exploring effective attention computation, norm-based analysis, and practical applications in neural networks.
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5 Lesons
36 minutes
On-Demand
Free-Video
UofU Data Science

Evaluating Input Attribution Methods in Machine Learning - Plausibility and Comprehensiveness

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Explore advanced concepts in gradient-based input attribution, from plausibility assessment to comprehensive evaluation methods like ROAR and Recursive-ROAR techniques.
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4 Lesons
37 minutes
On-Demand
Free-Video
UofU Data Science

Gradient-Based Input Attribution Methods and Their Applications

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Explore gradient-based input attribution methods, from fundamental concepts to practical applications, limitations, and advanced extensions in machine learning model interpretability.
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9 Lesons
58 minutes
On-Demand
Free-Video
Nicholas Renotte

Machine Learning Explainability & Bias Detection with Watson OpenScale

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Learn to monitor, explain, and debias machine learning models using Watson OpenScale. Explore performance metrics, interpret predictions, and detect model drift for improved AI transparency and reliability.
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11 Lesons
23 minutes
On-Demand
Free-Video
Prodramp

Develop Successful Deep Learning Projects with This 10 Steps Formula

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Learn a 10-step formula for successful deep learning projects, covering problem definition, data preparation, model selection, training strategies, regularization, augmentation, interpretability, and deployment.
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12 Lesons
22 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

LEMNA - Explaining Deep Learning Based Security Applications

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Explore LEMNA, a novel technique for explaining deep learning models in security applications, addressing transparency concerns and improving interpretability for critical decisions.
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13 Lesons
21 minutes
On-Demand
Free-Video
Simons Institute

Interpretability - Now What?

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Explore machine learning interpretability methods, focusing on Testing with Concept Activation Vectors (TCAV) and its applications in image prediction and medical diagnosis.
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21 Lesons
48 minutes
On-Demand
Free-Video
Launchpad

Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks

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Explore texture bias in CNNs: origins, prevalence, and implications. Examine dataset influence, training objectives, and model architectures to understand and mitigate this phenomenon in computer vision.
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13 Lesons
26 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Explainable Structured Machine Learning in Similarity, Graph and Transformer Models

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Explores techniques for explaining complex machine learning models, focusing on similarity, graph, and transformer structures. Discusses applications in knowledge evolution, gender bias, and task-solving.
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1 Lesons
53 minutes
On-Demand
Free-Video
Databricks

Interpretable Machine Learning - Data Brew Season 2 Episode 7

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Explore interpretable machine learning with Ameet Talwalkar, discussing its relation to data privacy, fairness, and practical applications in this insightful interview on cutting-edge ML research.
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12 Lesons
37 minutes
On-Demand
Free-Video
Linux Foundation

Mitigating Bias in Models with SHAP and Fairlearn

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Learn techniques to identify and mitigate bias in machine learning models using SHAP and Fairlearn, enhancing fairness and transparency in AI systems.
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1 Lesons
42 minutes
On-Demand
Free-Video
IEEE

SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks

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Explore robust algorithms for privacy-preserved federated learning in future networks, focusing on explainable and secure data collaboration techniques.
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1 Lesons
15 minutes
On-Demand
Free-Video
Alan Turing Institute

Machine Learning - From Black Boxes to White Boxes - Mihaela van der Schaar

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Exploring innovative approaches to make complex machine learning models interpretable and trustworthy, especially in medical applications, for diverse users including clinicians and patients.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
Center for Language & Speech Processing(CLSP), JHU

Model Distillation for Faithful Explanations of Medical Code Predictions

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Explore knowledge distillation techniques for generating faithful explanations of medical code predictions, enhancing trust in machine learning models for clinical decision-making.
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1 Lesons
12 minutes
On-Demand
Free-Video
Simons Institute

Linguistic Interaction in Humans and Machines - Understanding Higher-Level Intelligence

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Exploring methods from cognitive science to study ML models and human cognition, focusing on language-based projects that analyze adaptation and generalization in linguistic interactions.
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1 Lesons
51 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

Model Explanations with Differential Privacy

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Explore techniques for generating privacy-preserving explanations of machine learning models, balancing transparency with data protection in AI systems.
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1 Lesons
15 minutes
On-Demand
Free-Video
GOTO Conferences

Ethical ML - Who's Afraid of the Black Box Models

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Explore ethical machine learning beyond interpretability, focusing on responsible data handling and fairness testing to meet GDPR and ALTAI guidelines while using black-box models.
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9 Lesons
39 minutes
On-Demand
Free-Video
EuroPython Conference

Trust Me, I'm a Data Scientist - Ethics for Builders of Data-Based Applications

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Explore ethical pitfalls in data science through a practical example, covering algorithmic fairness, model interpretability, and handling minority classes in AI applications.
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14 Lesons
25 minutes
On-Demand
Free-Video
NDC Conferences

ML BuzzWords Demystified

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Demystifies latest ML trends like Reinforcement Learning, AutoML, and ML bias. Provides clear insights into complex concepts, ensuring you're up-to-date with the field's advancements.
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39 Lesons
53 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Sensitivity and Interpretability of AI Models in Regulated Industries

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Explore sensitivity analysis for AI model interpretability in regulated industries. Learn design and implementation approaches using case studies from healthcare and human activity data.
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1 Lesons
22 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Responsible AI: Challenges and Emerging Practices - Keynote

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Explore challenges and emerging practices in responsible AI, including unintended consequences, model interpretability, fairness, and engineering objectives.
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1 Lesons
27 minutes
On-Demand
Free-Video
Data Science Festival

Where Did My Model Go Wrong? Toolkits of Interpretability, Bias and Fairness to the Rescue

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Explore techniques to identify and correct biases in machine learning models, interpret results, and ensure fair outcomes in model development and deployment.
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1 Lesons
38 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Interpretable Self-Explanatory Machine Learning Models

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Explore interpretable machine learning models that are self-explanatory, focusing on making complex neural networks more transparent and understandable for critical applications.
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1 Lesons
32 minutes
On-Demand
Free-Video
Cooperative AI Foundation

Computational Social Choice and AI Value Learning

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Explore computational social choice and AI value learning, focusing on misspecification in RLHF, model interpretability, and foundation model capability evaluations.
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1 Lesons
55 minutes
On-Demand
Free-Video
Data Council

How to Interpret and Explain Black Box Models

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Explore popular model explanation techniques for machine learning interpretability, including LIME, SHAP, and visual analytics. Gain insights into making complex models more understandable and explainable.
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1 Lesons
31 minutes
On-Demand
Free-Video
IIIA Hub

A Journey Towards Explainable AI and Its Societal Implications - From Trust Issues to Complex Models

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Explore the principles of explainable AI, from feature-based to data-driven models, examining key algorithms like LIME and Shapley while understanding their broader societal impact and applications in search systems.
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30 Lesons
1 hour 13 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Responsible AI Toolbox: Practical Approaches and Tools for Ethical Machine Learning

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Explore practical approaches to responsible AI and learn how open-source and cloud-integrated capabilities empower data scientists to understand and improve ML models ethically.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
USENIX

DNN-GP: Diagnosing and Mitigating Model's Faults Using Latent Concepts

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Explore an innovative fault diagnosis tool for Deep Neural Networks that interprets latent concepts to detect and mitigate various model vulnerabilities, from adversarial attacks to concept drift.
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1 Lesons
11 minutes
On-Demand
Free-Video
Simons Institute

Scalably Understanding AI with AI - Using AI Systems for Model Behavior Analysis

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Explore advanced techniques for analyzing AI systems, from behavior elicitation to neuron description, focusing on using AI tools to understand and improve AI model behaviors and representations.
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1 Lesons
46 minutes
On-Demand
Free-Video