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Found 21 papers, 6 papers with code
A Relation Extraction Dataset for Knowledge Extraction from Web Tables
Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set.
Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation
Efficiently generating grasp poses tailored to specific regions of an object is vital for various robotic manipulation tasks, especially in a dual-arm setup.
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects.
Unsupervised clustering of disturbances in power systems via deep convolutional autoencoders
Using neural networks with machine learning can aid in accurately classifying the recorded waveforms and help power system engineers diagnose and rectify the root causes of problems.
SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping
Unlike existing methods that depend on an external canonicalization, SCARP performs canonicalization, pose estimation, and shape completion in a single network, improving the performance by 45% over the existing baselines.
Objects as Spatio-Temporal 2.5D points
Determining accurate bird's eye view (BEV) positions of objects and tracks in a scene is vital for various perception tasks including object interactions mapping, scenario extraction etc., however, the level of supervision required to accomplish that is extremely challenging to procure.
Multi-Static UWB Radar-based Passive Human Tracking Using COTS Devices
Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization.
Relation Extraction from Tables using Artificially Generated Metadata
To address this issue, we propose methods to artificially create some of this metadata for synthetic tables.
Sentiment Analysis of Code-Mixed Social Media Text (Hinglish)
The models were created using various machine learning algorithms such as SVM, KNN, Decision Trees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting classifiers.
Optimization of Radar Parameters for Maximum Detection Probability Under Generalized Discrete Clutter Conditions Using Stochastic Geometry
Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section.
Simulating multiscale gated field emitters -- a hybrid approach
For a compound geometry with local field enhancement by a factor of around 1000, a hybrid model is used where the vacuum field calculated using COMSOL is imported into the Particle-In-Cell code PASUPAT where the emission module is implemented.
Applied Physics Mesoscale and Nanoscale Physics Accelerator Physics Computational Physics Plasma Physics
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)
This paper discusses the design of the system used for providing a solution for the problem given at SemEval-2020 Task 9 where sentiment analysis of code-mixed language Hindi and English needed to be performed.
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation
The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances.
Relation Extraction using Explicit Context Conditioning
Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them.
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i. e., an ontology).
Faster Convergence & Generalization in DNNs
Deep neural networks have gained tremendous popularity in last few years.
Improving Active Learning in Systematic Reviews
The first step in a systematic review task is to identify all the studies relevant to the review.
Predicting Branch Visits and Credit Card Up-selling using Temporal Banking Data
There is an abundance of temporal and non-temporal data in banking (and other industries), but such temporal activity data can not be used directly with classical machine learning models.
Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings
In a traditional setting, classifiers are trained to approximate a target function $f:X \rightarrow Y$ where at least a sample for each $y \in Y$ is presented to the training algorithm.
Efficient Document Indexing Using Pivot Tree
We present a novel method for efficiently searching top-k neighbors for documents represented in high dimensional space of terms based on the cosine similarity.
