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Found 19 papers, 12 papers with code
AttentionViz: A Global View of Transformer Attention
Transformer models are revolutionizing machine learning, but their inner workings remain mysterious.
The System Model and the User Model: Exploring AI Dashboard Design
We conjecture that, for many systems, the two most important models will be of the user and of the system itself.
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
Language models show a surprising range of capabilities, but the source of their apparent competence is unclear.
Toy Models of Superposition
Neural networks often pack many unrelated concepts into a single neuron - a puzzling phenomenon known as 'polysemanticity' which makes interpretability much more challenging.
Interpreting a Machine Learning Model for Detecting Gravitational Waves
We describe a case study of translational research, applying interpretability techniques developed for computer vision to machine learning models used to search for and find gravitational waves.
An Interpretability Illusion for BERT
We describe an "interpretability illusion" that arises when analyzing the BERT model.
The What-If Tool: Interactive Probing of Machine Learning Models
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs.
Visualizing and Measuring the Geometry of BERT
Transformer architectures show significant promise for natural language processing.
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure
The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate despite their ecological validity.
TensorFlow.js: Machine Learning for the Web and Beyond
TensorFlow. js is a library for building and executing machine learning algorithms in JavaScript.
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology.
Adversarial Spheres
We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.
TCAV: Relative concept importance testing with Linear Concept Activation Vectors
In particular, this framework enables non-machine learning experts to express concepts of interests and test hypotheses using examples (e. g., a set of pictures that illustrate the concept).
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
The interpretation of deep learning models is a challenge due to their size, complexity, and often opaque internal state.
Direct-Manipulation Visualization of Deep Networks
The recent successes of deep learning have led to a wave of interest from non-experts.
SmoothGrad: removing noise by adding noise
Explaining the output of a deep network remains a challenge.
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications.
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
