Works at IIT Delhi on computational/mathematical models of learning, information processing and decision-making in complex systems (biological, cognitive, social).
For the task of predicting a reference sentence amidst grammatical variants, what is the role of ... more For the task of predicting a reference sentence amidst grammatical variants, what is the role of Uniform Information Density (UID) effects?
The main subject and the associated verb in English must agree in grammatical number as per the S... more The main subject and the associated verb in English must agree in grammatical number as per the Subject-Verb Agreement (SVA) phenomenon. It has been found that the presence of a noun between the verb and the main subject, whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening noun rather than the main noun; the former thus acts as an agreement attractor. Such attractors have also been shown to pose a challenge for RNN models without explicit hierarchical bias to perform well on SVA tasks. Previous work suggests that syntactic cues in the input can aid such models to choose hierarchical rules over linear rules for number agreement. In this work, we investigate the effects of the choice of training data, training algorithm, and architecture on hierarchical generalization. We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on nat...
According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 201... more 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. The prior works cited above studied syntactic reduction in language production at particular choice points in a sentence. In contrast, we use a variant of the above UID hypothesis in order to investigate the extent to which word order choices in Hindi are influenced by the drive to minimize the variance of information across entire sentences. To this end, we propose multiple lexical and syntactic measures (at both word and constituent levels) to capture the uniform spread of information across a sentence. Subsequently, we incorporate these measures in machine learning models aimed to distinguish between a naturally occurring corpus sentence and its grammatical variants (expressing the same idea). Our results indicate that our UID measures are not a significant factor in predicting th...
Deep extreme multi-label learning (XML) requires training deep architectures that can tag a data ... more Deep extreme multi-label learning (XML) requires training deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. XML applications such as ad and product recommendation involve labels rarely seen during training but which nevertheless hold the key to recommendations that delight users. Effective utilization of label metadata and high quality predictions for rare labels at the scale of millions of labels are thus key challenges in contemporary XML research. To address these, this paper develops the SiameseXML framework based on a novel probabilistic model that naturally motivates a modular approach melding Siamese architectures with high-capacity extreme classifiers, and a training pipeline that effortlessly scales to tasks with 100 million labels. SiameseXML offers predictions 2–13% more accurate than leading XML methods on public benchmark datasets, as well as in live A/B tests on the Bing search engine, it offers sign...
Gene Regulatory Networks (GRNs) hold the key to understanding and solving many problems in biolog... more Gene Regulatory Networks (GRNs) hold the key to understanding and solving many problems in biological sciences, with critical applications in medicine and therapeutics. However, discovering GRNs in the laboratory is a cumbersome and tricky affair, since the number of genes and interactions, say in a mammalian cell, are very large. We aim to discover these GRNs computationally, by using gene expression levels as a “time-series” dataset. We research and employ techniques from probability and information theory, theory of dynamical systems, and graph structure estimation, to establish causal relations between genes, on synthetic datasets. Furthermore, we suggest methods for global estimation of gene networks. Therefore, narrowing the space of genetic interactions to be looked at when discovering these GRNs in the lab.
We investigate the relative impact of two influential theories of language comprehension, viz., D... more We investigate the relative impact of two influential theories of language comprehension, viz., Dependency Locality Theory(Gibson 2000; DLT) and Surprisal Theory (Hale 2001, Levy 2008), on preverbal constituent ordering in Hindi, a predominantly SOV language with flexible word order. Prior work in Hindi has shown that word order scrambling is influenced by information structure constraints in discourse. However, the impact of cognitively grounded factors on Hindi constituent ordering is relatively underexplored. We test the hypothesis that dependency length minimization is a significant predictor of syntactic choice, once information status and surprisal measures (estimated from n-gram i.e., trigram and incremental dependency parsing models) have been added to a machine learning model. Towards this end, we setup a framework to generate meaning-equivalent grammatical variants of Hindi sentences by linearizing preverbal constituents of projective dependency trees in the Hindi-Urdu Tre...
High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and... more High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor. This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T-W, T-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgerie...
Incomplete immunisation coverage causes preventable illness and death in both developing and deve... more Incomplete immunisation coverage causes preventable illness and death in both developing and developed countries. Identification of factors that might modulate coverage could inform effective immunisation programmes and policies. We constructed a performance indicator that could quantitatively approximate measures of the susceptibility of immunisation programmes to coverage losses, with an aim to identify correlations between trends in vaccine coverage and socioeconomic factors. We undertook a data-driven time-series analysis to examine trends in coverage of diphtheria, tetanus, and pertussis (DTP) vaccination across 190 countries over the past 30 years. We grouped countries into six world regions according to WHO classifications. We used Gaussian process regression to forecast future coverage rates and provide a vaccine performance index: a summary measure of the strength of immunisation coverage in a country. Overall vaccine coverage increased in all six world regions between 1980...
Abstract A key question in modern biology is how the complexity of protein-protein interaction ne... more Abstract A key question in modern biology is how the complexity of protein-protein interaction net-works relates to biological functionality. One way of understanding the set of proteins and their interactions (the interactome) is to look at them as a network of nodes connected by ...
For the task of predicting a reference sentence amidst grammatical variants, what is the role of ... more For the task of predicting a reference sentence amidst grammatical variants, what is the role of Uniform Information Density (UID) effects?
The main subject and the associated verb in English must agree in grammatical number as per the S... more The main subject and the associated verb in English must agree in grammatical number as per the Subject-Verb Agreement (SVA) phenomenon. It has been found that the presence of a noun between the verb and the main subject, whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening noun rather than the main noun; the former thus acts as an agreement attractor. Such attractors have also been shown to pose a challenge for RNN models without explicit hierarchical bias to perform well on SVA tasks. Previous work suggests that syntactic cues in the input can aid such models to choose hierarchical rules over linear rules for number agreement. In this work, we investigate the effects of the choice of training data, training algorithm, and architecture on hierarchical generalization. We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on nat...
According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 201... more 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. The prior works cited above studied syntactic reduction in language production at particular choice points in a sentence. In contrast, we use a variant of the above UID hypothesis in order to investigate the extent to which word order choices in Hindi are influenced by the drive to minimize the variance of information across entire sentences. To this end, we propose multiple lexical and syntactic measures (at both word and constituent levels) to capture the uniform spread of information across a sentence. Subsequently, we incorporate these measures in machine learning models aimed to distinguish between a naturally occurring corpus sentence and its grammatical variants (expressing the same idea). Our results indicate that our UID measures are not a significant factor in predicting th...
Deep extreme multi-label learning (XML) requires training deep architectures that can tag a data ... more Deep extreme multi-label learning (XML) requires training deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. XML applications such as ad and product recommendation involve labels rarely seen during training but which nevertheless hold the key to recommendations that delight users. Effective utilization of label metadata and high quality predictions for rare labels at the scale of millions of labels are thus key challenges in contemporary XML research. To address these, this paper develops the SiameseXML framework based on a novel probabilistic model that naturally motivates a modular approach melding Siamese architectures with high-capacity extreme classifiers, and a training pipeline that effortlessly scales to tasks with 100 million labels. SiameseXML offers predictions 2–13% more accurate than leading XML methods on public benchmark datasets, as well as in live A/B tests on the Bing search engine, it offers sign...
Gene Regulatory Networks (GRNs) hold the key to understanding and solving many problems in biolog... more Gene Regulatory Networks (GRNs) hold the key to understanding and solving many problems in biological sciences, with critical applications in medicine and therapeutics. However, discovering GRNs in the laboratory is a cumbersome and tricky affair, since the number of genes and interactions, say in a mammalian cell, are very large. We aim to discover these GRNs computationally, by using gene expression levels as a “time-series” dataset. We research and employ techniques from probability and information theory, theory of dynamical systems, and graph structure estimation, to establish causal relations between genes, on synthetic datasets. Furthermore, we suggest methods for global estimation of gene networks. Therefore, narrowing the space of genetic interactions to be looked at when discovering these GRNs in the lab.
We investigate the relative impact of two influential theories of language comprehension, viz., D... more We investigate the relative impact of two influential theories of language comprehension, viz., Dependency Locality Theory(Gibson 2000; DLT) and Surprisal Theory (Hale 2001, Levy 2008), on preverbal constituent ordering in Hindi, a predominantly SOV language with flexible word order. Prior work in Hindi has shown that word order scrambling is influenced by information structure constraints in discourse. However, the impact of cognitively grounded factors on Hindi constituent ordering is relatively underexplored. We test the hypothesis that dependency length minimization is a significant predictor of syntactic choice, once information status and surprisal measures (estimated from n-gram i.e., trigram and incremental dependency parsing models) have been added to a machine learning model. Towards this end, we setup a framework to generate meaning-equivalent grammatical variants of Hindi sentences by linearizing preverbal constituents of projective dependency trees in the Hindi-Urdu Tre...
High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and... more High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor. This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T-W, T-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgerie...
Incomplete immunisation coverage causes preventable illness and death in both developing and deve... more Incomplete immunisation coverage causes preventable illness and death in both developing and developed countries. Identification of factors that might modulate coverage could inform effective immunisation programmes and policies. We constructed a performance indicator that could quantitatively approximate measures of the susceptibility of immunisation programmes to coverage losses, with an aim to identify correlations between trends in vaccine coverage and socioeconomic factors. We undertook a data-driven time-series analysis to examine trends in coverage of diphtheria, tetanus, and pertussis (DTP) vaccination across 190 countries over the past 30 years. We grouped countries into six world regions according to WHO classifications. We used Gaussian process regression to forecast future coverage rates and provide a vaccine performance index: a summary measure of the strength of immunisation coverage in a country. Overall vaccine coverage increased in all six world regions between 1980...
Abstract A key question in modern biology is how the complexity of protein-protein interaction ne... more Abstract A key question in modern biology is how the complexity of protein-protein interaction net-works relates to biological functionality. One way of understanding the set of proteins and their interactions (the interactome) is to look at them as a network of nodes connected by ...
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