Publications (12)
Beyond network structure: How heterogenous susceptibility modulates the spread of epidemics
Daniel Smilkov, Cesar A. Hidalgo, Ljupco Kocarev
The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences i…
SmoothGrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim +2
Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final dec…
Direct-Manipulation Visualization of Deep Networks
Daniel Smilkov, Shan Carter, D. Sculley +2
The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is impor…
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making
Carrie J. Cai, Emily Reif, Narayan Hegde +8
Machine learning (ML) is increasingly being used in image retrieval systems for medical decision making. One application of ML is to retrieve visually similar medical images from p…
Similar Image Search for Histopathology: SMILY
Narayan Hegde, Jason D. Hipp, Yun Liu +11
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Thou…
Rich-club and page-club coefficients for directed graphs
Daniel Smilkov, Ljupco Kocarev
Rich-club and page-club coefficients and their null models are introduced for directed graphs. Null models allow for a quantitative discussion of the rich-club and page-club phenom…
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Daniel Smilkov, Nikhil Thorat, Charles Nicholson +3
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties o…
Identifying communities by influence dynamics in social networks
Angel Stanoev, Daniel Smilkov, Ljupco Kocarev
Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algori…
The influence of the network topology on epidemic spreading
Daniel Smilkov, Ljupco Kocarev
The influence of the network's structure on the dynamics of spreading processes has been extensively studied in the last decade. Important results that partially answer this questi…
Analytically solvable processes on networks
Daniel Smilkov, Ljupco Kocarev
We introduce a broad class of analytically solvable processes on networks. In the special case, they reduce to random walk and consensus process - two most basic processes on netwo…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
TensorFlow.js: Machine Learning for the Web and Beyond
Daniel Smilkov, Nikhil Thorat, Yannick Assogba +17
TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The libra…