Chandan Singh | chandan singh


phd candidate at berkeley working on interpretable machine-learning across domains 🧠🦠🔭 -- let's do good with models


year title authors tags paper code slides
2020 Revisiting complexity and the bias-variance tradeoff dwivedi*, singh*, yu, and wainwright ml📊 arxiv --
2020 Curating a COVID-19 data repository and forecasting county-level death counts in the United States altieri et al. 📊🦠 hdsr
2020 interpretations are useful: penalizing explanations to align neural networks with prior knowledge rieger, singh, murdoch & yu 🔎 ml icml
2020 transformation importance with applications to cosmology singh*, ha*, lanusse, boehm, liu & yu 🔎 🌌 ml iclr workshop (spotlight)
2019 disentangled attribution curves for interpreting random forests and boosted trees devlin, singh, murdoch & yu 🔎 ml arxiv --
2019 interpretable machine learning: definitions, methods, and applications Murdoch*, Singh*, Kumbier, Abbasi-Asl, & Yu 🔎 ml pnas -- ,
2019 hierarchical interpretations for neural network predictions Singh*, Murdoch*, & Yu 🔎 ml ICLR ,
2018 large scale image segmentation with structured loss based deep learning for connectome reconstruction Funke*, Tschopp*, et al. ml 🧠 TPAMI
2018 linearization of excitatory synaptic integration at no extra cost Morel, Singh, & Levy 🧠 J Comp Neuro
2017 a consensus layer V pyramidal neuron can sustain interpulse-interval coding Singh & Levy 🧠 Plos One
2017 a constrained, weighted-l1 minimization approach for joint discovery of heterogeneous neural connectivity graphs Singh, Wang, & Qi ml 🧠 neurips Workshop ,

resources + posts

Some posts in ml research and organizing different papers.

Resources including teaching slides, research overview slides, and coding projects.

A rough set of notes which may serve as useful reference for people in machine learning / neuroscience.