Search Results for author:
Found 31 papers, 19 papers with code
FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders
We validate the proposed methods on the curated dataset, and report a new state-of-the-art (SOTA) accuracy of 96. 4% for the GBC detection problem, against an accuracy of 84% by current Image-based SOTA - GBCNet, and RadFormer, and 94. 7% by Video-based SOTA - AdaMAE.
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
This research explores the problem of deception through the lens of psychology, employing a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence.
Gall Bladder Cancer Detection from US Images with Only Image Level Labels
We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest.
Adversarial Adaptation for French Named Entity Recognition
We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization.
Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora
Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes.
An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning
Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance.
RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis.
Federated Continual Learning for Text Classification via Selective Inter-client Transfer
The objective of Federated Continual Learning (FCL) is to improve deep learning models over life time at each client by (relevant and efficient) knowledge transfer without sharing data.
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining
We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1. 5% compared to SOTA.
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning
However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor.
Ranked #1 on
Gallbladder Cancer Detection
on GBCU
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents.
TopicBERT for Energy Efficient Document Classification
Our work thus focuses on optimizing the computational cost of fine-tuning for document classification.
Neural Topic Modeling with Continual Lifelong Learning
To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data.
Explainable and Discourse Topic-aware Neural Language Understanding
Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019.
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
in topic modeling, (2) A novel lifelong learning mechanism into neural topic modeling framework to demonstrate continuous learning in sequential document collections and minimizing catastrophic forgetting.
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
Neural Architectures for Fine-Grained Propaganda Detection in News
To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e. g., CNN, LSTM-CRF and BERT) and extract linguistic (e. g., part-of-speech, named entity, readability, sentiment, emotion, etc.
Neural Relation Extraction Within and Across Sentence Boundaries
iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries.
Ranked #1 on
Relation Extraction
on MUC6
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
Document Informed Neural Autoregressive Topic Models
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks.
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation
Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks.
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts.
Joint Bootstrapping Machines for High Confidence Relation Extraction
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances.
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
What are the visual features underlying human versus machine vision?
Our study demonstrates that the narrowing gap between the object recognition accuracy of human observers and DCNs obscures distinct visual strategies used by each to achieve this performance.
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.
Combining Recurrent and Convolutional Neural Networks for Relation Classification
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks.
