122 citations · 258 across the 19 of their papers we have counts for
20 papers
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation
Bo Peng, Srinivasan Parthasarathy, Xia Ning
Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-ba…
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ziqi Chen, Bo Peng, Srinivasan Parthasarathy +1
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules. In this paper, we formulated an in silico shape-conditioned molecule…
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran +1
Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been prop…
FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
Yuntian He, Saket Gurukar, Srinivasan Parthasarathy
Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased r…
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation
Bo Peng, Srinivasan Parthasarathy, Xia Ning
Sequential recommendation aims to recommend the next item of users' interest based on their historical interactions. Recently, the self-attention mechanism has been adapted for seq…
Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation
Bo Peng, Chang-Yu Tai, Srinivasan Parthasarathy +1
Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop prospective preference e…
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
Saket Gurukar, Nikil Pancha, Andrew Zhai +5
Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several…
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus
Thilini Wijesiriwardene, Vinh Nguyen, Goonmeet Bajaj +7
The UMLS Metathesaurus integrates more than 200 biomedical source vocabularies. During the Metathesaurus construction process, synonymous terms are clustered into concepts by human…
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
Sean Current, Yuntian He, Saket Gurukar +1
As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated…
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
Pranav Maneriker, Yuntian He, Srinivasan Parthasarathy
Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host the…
Disentanglement for audio-visual emotion recognition using multitask setup
Raghuveer Peri, Srinivas Parthasarathy, Charles Bradshaw +1
Deep learning models trained on audio-visual data have been successfully used to achieve state-of-the-art performance for emotion recognition. In particular, models trained with mu…
Detecting expressions with multimodal transformers
Srinivas Parthasarathy, Shiva Sundaram
Developing machine learning algorithms to understand person-to-person engagement can result in natural user experiences for communal devices such as Amazon Alexa. Among other cues…
Training Strategies to Handle Missing Modalities for Audio-Visual Expression Recognition
Srinivas Parthasarathy, Shiva Sundaram
Automatic audio-visual expression recognition can play an important role in communication services such as tele-health, VOIP calls and human-machine interaction. Accuracy of audio-…
Self-Supervised learning with cross-modal transformers for emotion recognition
Aparna Khare, Srinivas Parthasarathy, Shiva Sundaram
Emotion recognition is a challenging task due to limited availability of in-the-wild labeled datasets. Self-supervised learning has shown improvements on tasks with limited labeled…
Multi-modal embeddings using multi-task learning for emotion recognition
Aparna Khare, Srinivas Parthasarathy, Shiva Sundaram
General embeddings like word2vec, GloVe and ELMo have shown a lot of success in natural language tasks. The embeddings are typically extracted from models that are built on general…
Multiresolution and Multimodal Speech Recognition with Transformers
Georgios Paraskevopoulos, Srinivas Parthasarathy, Aparna Khare +1
This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We particularly focus on the scene context provided by the…
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy +1
Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed mo…
Semi-Supervised Speech Emotion Recognition with Ladder Networks
Srinivas Parthasarathy, Carlos Busso
Speech emotion recognition (SER) systems find applications in various fields such as healthcare, education, and security and defense. A major drawback of these systems is their lac…
Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional Attributes
Srinivas Parthasarathy, Carlos Busso
Recognizing emotions using few attribute dimensions such as arousal, valence and dominance provides the flexibility to effectively represent complex range of emotional behaviors. C…
MILE: A Multi-Level Framework for Scalable Graph Embedding
Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy
Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational com…