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
'20 Overcoming confounding in face datasets via GAN-based matching singh, balakrishnan, & perona ml in prep
'20 Revisiting complexity and the bias-variance tradeoff dwivedi*, singh*, yu, and wainwright ml arxiv
'20 Evaluating the Predictive and Descriptive Accuracy of Clinical Decision Rules to Identify Children with Blunt Abdominal Trauma that are at Very Low Risk for Intra-abdominal Injury kornblith, singh et al. 📊 🔎 💊 in prep
'20 Predicting successful clathrin-coated pits in clathrin-mediated endocytosis via auxilin li*, singh*, et al. 📊 🦠 in prep
'20 Curating a COVID-19 data repository and forecasting county-level death counts in the United States altieri et al. 📊 🦠 hdsr
'20 interpretations are useful: penalizing explanations to align neural networks with prior knowledge rieger, singh, murdoch & yu 🔎 ml icml
'20 transformation importance with applications to cosmology singh*, ha*, lanusse, boehm, liu & yu 🔎 🌌 ml iclr workshop (spotlight)
'19 disentangled attribution curves for interpreting random forests and boosted trees devlin, singh, murdoch & yu 🔎 ml arxiv
'19 interpretable machine learning: definitions, methods, and applications Murdoch*, Singh*, Kumbier, Abbasi-Asl, & Yu 🔎 ml pnas ,
'19 hierarchical interpretations for neural network predictions Singh*, Murdoch*, & Yu 🔎 ml ICLR ,
'18 large scale image segmentation with structured loss based deep learning for connectome reconstruction Funke*, Tschopp*, et al. ml 🧠 TPAMI
'18 linearization of excitatory synaptic integration at no extra cost Morel, Singh, & Levy 🧠 J Comp Neuro
'17 a consensus layer V pyramidal neuron can sustain interpulse-interval coding Singh & Levy 🧠 Plos One
'17 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.