Search Results for author:
Found 11 papers, 7 papers with code
The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks
We achieve this through a novel analytical model for predicting sparse network performance, and validate the predicted speedup using several real-world computer vision architectures pruned across a range of sparsity patterns and degrees.
Robust Learning in Heterogeneous Contexts
We consider the problem of learning from training data obtained in different contexts, where the underlying context distribution is unknown and is estimated empirically.
Robust Localization in Wireless Networks From Corrupted Signals
We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by nonideal signal conditions.
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space.
Learning Robust Decision Policies from Observational Data
We address the problem of learning a decision policy from observational data of past decisions in contexts with features and associated outcomes.
DENS: A Dataset for Multi-class Emotion Analysis
Our results show that the dataset provides a novel opportunity in emotion analysis that requires moving beyond existing sentence-level techniques.
Exploring Multilingual Syntactic Sentence Representations
We study methods for learning sentence embeddings with syntactic structure.
Robust Risk Minimization for Statistical Learning
We consider a general statistical learning problem where an unknown fraction of the training data is corrupted.
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data.
Learning Localized Spatio-Temporal Models From Streaming Data
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream.
Unsupervised Cipher Cracking Using Discrete GANs
This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext.
