Search Results for author:
Found 21 papers, 5 papers with code
Dual Mechanism Priming Effects in Hindi Word Order
By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.
Discourse Context Predictability Effects in Hindi Word Order
While prior work has shown that a number of factors (e. g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature.
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
Multi-Modal Extreme Classification
On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding-only methods but mostly focus on text-based categorization tasks.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set.
DECAF: Deep Extreme Classification with Label Features
This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
ECLARE: Extreme Classification with Label Graph Correlations
This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?
However, we observe that several RNN types, including the ONLSTM which has a soft structural inductive bias, surprisingly fail to perform well on sentences without attractors when trained solely on sentences with attractors.
A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition
In the last few years, compression of deep neural networks has become an important strand of machine learning and computer vision research.
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?
Long short-term memory (LSTM) networks and their variants are capable of encapsulating long-range dependencies, which is evident from their performance on a variety of linguistic tasks.
Ranked #29 on
Language Modelling
on WikiText-103
(Validation perplexity metric)
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.
Surprisal and Interference Effects of Case Markers in Hindi Word Order
Based on the Production-Distribution-Comprehension (PDC) account of language processing, we formulate two distinct hypotheses about case marking, word order choices and processing in Hindi.
Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech
This study examines the role of three influential theories of language processing, \textit{viz.
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?
Visual scene understanding often requires the processing of human-object interactions.
Uniform Information Density Effects on Syntactic Choice in Hindi
According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 2010), speakers tend to distribute information density across the signal uniformly while producing language.
Modeling Image Virality with Pairwise Spatial Transformer Networks
The study of virality and information diffusion online is a topic gaining traction rapidly in the computational social sciences.
Linguistic features for Hindi light verb construction identification
To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus.
Examining Representational Similarity in ConvNets and the Primate Visual Cortex
We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate visual cortex.
