Search Results for author:
Found 18 papers, 6 papers with code
A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data
We propose a simple objective function for the optimization of the instance-centric CF generation problem.
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation.
Multi-Task Learning Improves Performance In Deep Argument Mining Models
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis.
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations
Here, we aim to explain these differences by analyzing the impact of these objectives on the structure and transferability of the learned representations.
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning.
UNet Based Pipeline for Lung Segmentation from Chest X-Ray Images
Biomedical image segmentation is one of the fastest growing fields which has seen extensive automation through the use of Artificial Intelligence.
Robust Planning for Human-Robot Joint Tasks with Explicit Reasoning on Human Mental State
We consider the human-aware task planning problem where a human-robot team is given a shared task with a known objective to achieve.
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method
In this paper, we introduce a diverse large-scale dataset for table detection with more than seven thousand samples containing a wide variety of table structures collected from many diverse sources.
Ranked #1 on
Table Detection
on STDW
Beyond neural scaling laws: beating power law scaling via data pruning
Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning.
An Ensemble Model for Face Liveness Detection
In this paper, we present a passive method to detect face presentation attack a. k. a face liveness detection using an ensemble deep learning technique.
A Comparative study of Hyper-Parameter Optimization Tools
For a robust performance of a model, it is necessary to find out the right hyper-parameter combination.
Neural Structure Mapping For Learning Abstract Visual Analogies
Our framework uses (1) a multi-task visual relationship encoder to extract constituent concepts from raw visual input in the source domain, and (2) a neural module net analogy inference engine to reason compositionally about the inferred relation in the target domain.
An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
We obtained state-of-the-art results on the subjectivity task by fine-tuning the BERT Language Model.
Ranked #2 on
Subjectivity Analysis
on SUBJ
Outage Probability Analysis of Uplink Cell-Free Massive MIMO Network with and without Pilot Contamination
The signal-to-interference-plus-noise ratio (SINR) of the CF-mMIMO system is approximated via a Log-normal distribution using a two-step moment matching method.
Dimensionality Reduction
Information Theory
Information Theory
Context-Aware Scene Graph Generation With Seq2Seq Transformers
In this task, the model needs to detect objects and predict visual relationships between them.
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN
In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization and instance segmentation of natural images, can be used to perform this task in a fast manner with effective results.
Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification
Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras.
Learning and Tuning Meta-heuristics in Plan Space Planning
The learned models can in turn be tuned online using a domain independent error correction approach to further enhance their informativeness.
