Rishabh Bhardwaj | BITS Pilani (original) (raw)

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Papers by Rishabh Bhardwaj

Enhancing the user experience is an essential task for application service providers. For instanc... more Enhancing the user experience is an essential task for application service providers. For instance, two users living wide apart may have different tastes of food. A food recommender mobile application installed on an edge device might want to learn from user feedback (reviews) to satisfy the client's needs pertaining to distinct domains. Retrieving user data comes at the cost of privacy while asking for model parameters trained on a user device becomes space inefficient at a large scale. In this work, we propose an approach to learn a central (global) model from the federation of (local) models which are trained on user-devices, without disclosing the local data or model parameters to the server. We propose a federation mechanism for the problems with natural similarity metric between the labels which commonly appear in natural language understanding (NLU) tasks. To learn the global model, the objective is to minimize the optimal transport cost of the global model's predicti...

Recognizing the cause behind emotions in text is a fundamental yet under-explored area of researc... more Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamic among the interlocutors. To this end, we introduce the task of recognizing emotion cause in conversations with an accompanying dataset named RECCON. Furthermore, we define different cause types based on the source of the causes and establish strong transformer-based baselines to address two different sub-tasks of RECCON: 1) Causal Span Extraction and 2) Causal Emotion Entailment. The dataset is available at https://github.com/declare-lab/RECCON.

The dataset consists of the ISFET sensor data utilized to train ML models for drift compensation.

ArXiv, 2021

Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transfor... more Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transformer’s predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to be non-unique attentions that make them un...

2020 IEEE 17th India Council International Conference (INDICON), 2020

This paper presents the performance enhancement of Si3N4-gate Ion-Sensitive Field-Effect Transist... more This paper presents the performance enhancement of Si3N4-gate Ion-Sensitive Field-Effect Transistor (IS-FET) based pH sensor using machine learning (ML) techniques. The temporal and temperature characteristics of the ISFET device are modeled using SPICE tool. The developed macromodel incorporates the electrochemical and device parameters, which enhances the robustness of the model in order to produce accurate characteristics of ISFET device over a wide temperature range as well as long term usage. The ISFET readout circuit tends to show a drift in the measured pH values with temperature and time variations.To make the device functioning more accurate, we incorporate state-of-the-art models based on ML techniques. We show how such auxiliary ML models reduce the effects of most common undesired ambience variations to get consistent output from CVCC (Constant Voltage Constant Current) Read Out Integrated Circuit (ROIC). We first simulate the quality data from the CVCC ROIC developed us...

2017 IEEE Regional Symposium on Micro and Nanoelectronics (RSM), 2017

This paper presents a new Machine Learning based temperature compensation technique for Ion-Sensi... more This paper presents a new Machine Learning based temperature compensation technique for Ion-Sensitive Field-Effect Transistor (ISFET). The circuit models for various electronic devices like MOSFET are available in commercial Technology Computer Aided Design (TCAD) tools such as LT-SPICE but no built-in model exists for ISFET. Considering SiO2 as the sensing film, an ISFET circuit model was created in LT-SPICE and simulations were carried out to obtain characteristic curves for SiO2 based ISFET. A Machine Learning (ML) model was trained using the data collected from the simulations performed using the ISFET macromodel in the read-out circuitry. The simulations were performed at various temperatures and the temperature drift behavior of ISFET was fed into the ML model. Constant pH (predicted by the system) curves were obtained when the device is tested for various pH (7 and 10) solutions at different ambient temperatures.

ArXiv, 2021

Enhancing the user experience is an essential task for application service providers. For instanc... more Enhancing the user experience is an essential task for application service providers. For instance, two users living wide apart may have different tastes of food. A food recommender mobile application installed on an edge device might want to learn from user feedback (reviews) to satisfy the client’s needs pertaining to distinct domains. Retrieving user data comes at the cost of privacy while asking for model parameters trained on a user device becomes space inefficient at a large scale. In this work, we propose an approach to learn a central (global) model from the federation of (local) models which are trained on user-devices, without disclosing the local data or model parameters to the server. We propose a federation mechanism for the problems with natural similarity metric between the labels which commonly appear in natural language understanding (NLU) tasks. To learn the global model, the objective is to minimize the optimal transport cost of the global model’s predictions from...

Cogn. Comput., 2021

Contextual language models (CLMs) have pushed the NLP benchmarks to a new height. It has become a... more Contextual language models (CLMs) have pushed the NLP benchmarks to a new height. It has become a new norm to utilize CLM provided word embeddings in downstream tasks such as text classification. However, unless addressed, CLMs are prone to learn intrinsic gender-bias in the dataset. As a result, predictions of downstream NLP models can vary noticeably by varying gender words, such as replacing "he" to "she", or even gender-neutral words. In this paper, we focus our analysis on a popular CLM, i.e., BERT. We analyse the gender-bias it induces in five downstream tasks related to emotion and sentiment intensity prediction. For each task, we train a simple regressor utilizing BERT's word embeddings. We then evaluate the gender-bias in regressors using an equity evaluation corpus. Ideally and from the specific design, the models should discard gender informative features from the input. However, the results show a significant dependence of the system's predict...

IEEE Transactions on Industry Applications

Neural Computing and Applications

Journal of Computational Electronics

International Journal of Circuit Theory and Applications

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we investigate the importance of social network information compared to content in... more In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user's occupational class. We show that the content information of a user's tweets, the profile descriptions of a user's follower/following community, and the user's social network provide useful information for classifying a user's occupational group. In our study, we extend an existing dataset for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this dataset with just a small fraction of the training data.

Enhancing the user experience is an essential task for application service providers. For instanc... more Enhancing the user experience is an essential task for application service providers. For instance, two users living wide apart may have different tastes of food. A food recommender mobile application installed on an edge device might want to learn from user feedback (reviews) to satisfy the client's needs pertaining to distinct domains. Retrieving user data comes at the cost of privacy while asking for model parameters trained on a user device becomes space inefficient at a large scale. In this work, we propose an approach to learn a central (global) model from the federation of (local) models which are trained on user-devices, without disclosing the local data or model parameters to the server. We propose a federation mechanism for the problems with natural similarity metric between the labels which commonly appear in natural language understanding (NLU) tasks. To learn the global model, the objective is to minimize the optimal transport cost of the global model's predicti...

Recognizing the cause behind emotions in text is a fundamental yet under-explored area of researc... more Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamic among the interlocutors. To this end, we introduce the task of recognizing emotion cause in conversations with an accompanying dataset named RECCON. Furthermore, we define different cause types based on the source of the causes and establish strong transformer-based baselines to address two different sub-tasks of RECCON: 1) Causal Span Extraction and 2) Causal Emotion Entailment. The dataset is available at https://github.com/declare-lab/RECCON.

The dataset consists of the ISFET sensor data utilized to train ML models for drift compensation.

ArXiv, 2021

Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transfor... more Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transformer’s predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to be non-unique attentions that make them un...

2020 IEEE 17th India Council International Conference (INDICON), 2020

This paper presents the performance enhancement of Si3N4-gate Ion-Sensitive Field-Effect Transist... more This paper presents the performance enhancement of Si3N4-gate Ion-Sensitive Field-Effect Transistor (IS-FET) based pH sensor using machine learning (ML) techniques. The temporal and temperature characteristics of the ISFET device are modeled using SPICE tool. The developed macromodel incorporates the electrochemical and device parameters, which enhances the robustness of the model in order to produce accurate characteristics of ISFET device over a wide temperature range as well as long term usage. The ISFET readout circuit tends to show a drift in the measured pH values with temperature and time variations.To make the device functioning more accurate, we incorporate state-of-the-art models based on ML techniques. We show how such auxiliary ML models reduce the effects of most common undesired ambience variations to get consistent output from CVCC (Constant Voltage Constant Current) Read Out Integrated Circuit (ROIC). We first simulate the quality data from the CVCC ROIC developed us...

2017 IEEE Regional Symposium on Micro and Nanoelectronics (RSM), 2017

This paper presents a new Machine Learning based temperature compensation technique for Ion-Sensi... more This paper presents a new Machine Learning based temperature compensation technique for Ion-Sensitive Field-Effect Transistor (ISFET). The circuit models for various electronic devices like MOSFET are available in commercial Technology Computer Aided Design (TCAD) tools such as LT-SPICE but no built-in model exists for ISFET. Considering SiO2 as the sensing film, an ISFET circuit model was created in LT-SPICE and simulations were carried out to obtain characteristic curves for SiO2 based ISFET. A Machine Learning (ML) model was trained using the data collected from the simulations performed using the ISFET macromodel in the read-out circuitry. The simulations were performed at various temperatures and the temperature drift behavior of ISFET was fed into the ML model. Constant pH (predicted by the system) curves were obtained when the device is tested for various pH (7 and 10) solutions at different ambient temperatures.

ArXiv, 2021

Enhancing the user experience is an essential task for application service providers. For instanc... more Enhancing the user experience is an essential task for application service providers. For instance, two users living wide apart may have different tastes of food. A food recommender mobile application installed on an edge device might want to learn from user feedback (reviews) to satisfy the client’s needs pertaining to distinct domains. Retrieving user data comes at the cost of privacy while asking for model parameters trained on a user device becomes space inefficient at a large scale. In this work, we propose an approach to learn a central (global) model from the federation of (local) models which are trained on user-devices, without disclosing the local data or model parameters to the server. We propose a federation mechanism for the problems with natural similarity metric between the labels which commonly appear in natural language understanding (NLU) tasks. To learn the global model, the objective is to minimize the optimal transport cost of the global model’s predictions from...

Cogn. Comput., 2021

Contextual language models (CLMs) have pushed the NLP benchmarks to a new height. It has become a... more Contextual language models (CLMs) have pushed the NLP benchmarks to a new height. It has become a new norm to utilize CLM provided word embeddings in downstream tasks such as text classification. However, unless addressed, CLMs are prone to learn intrinsic gender-bias in the dataset. As a result, predictions of downstream NLP models can vary noticeably by varying gender words, such as replacing "he" to "she", or even gender-neutral words. In this paper, we focus our analysis on a popular CLM, i.e., BERT. We analyse the gender-bias it induces in five downstream tasks related to emotion and sentiment intensity prediction. For each task, we train a simple regressor utilizing BERT's word embeddings. We then evaluate the gender-bias in regressors using an equity evaluation corpus. Ideally and from the specific design, the models should discard gender informative features from the input. However, the results show a significant dependence of the system's predict...

IEEE Transactions on Industry Applications

Neural Computing and Applications

Journal of Computational Electronics

International Journal of Circuit Theory and Applications

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we investigate the importance of social network information compared to content in... more In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user's occupational class. We show that the content information of a user's tweets, the profile descriptions of a user's follower/following community, and the user's social network provide useful information for classifying a user's occupational group. In our study, we extend an existing dataset for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this dataset with just a small fraction of the training data.