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Found 19 papers, 4 papers with code
Improving Neural Machine Translation for Sanskrit-English
Sanskrit is one of the oldest languages of the Asian Subcontinent that fell out of common usage around 600 B. C.
Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness-the semantic coherence of generated images to the prompts they were conditioned on.
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.
OCTO+: A Suite for Automatic Open-Vocabulary Object Placement in Mixed Reality
We also introduce a benchmark for automatically evaluating the placement of virtual objects in augmented reality, alleviating the need for costly user studies.
OCTOPUS: Open-vocabulary Content Tracking and Object Placement Using Semantic Understanding in Mixed Reality
One key challenge in augmented reality is the placement of virtual content in natural locations.
SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework
We found that the proposed model improved results on all four metrics.
WikiWhy: Answering and Explaining Cause-and-Effect Questions
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging.
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs
Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities.
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces
However, connectionist models struggle to include explicit domain knowledge for deductive reasoning.
Multilevel profiling of situation and dialogue-based deep networks for movie genre classification using movie trailers
Short duration movie trailers provide useful insights about the movie as video content consists of the cognitive and the affective level features.
"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit
During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs).
Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?
In this paper, we describe our Knowledge Tracing model for the 2020 NeurIPS Education Challenge.
Interacting Conformal Carrollian Theories: Cues from Electrodynamics
We construct the free Lagrangian of the magnetic sector of Carrollian electrodynamics.
High Energy Physics - Theory General Relativity and Quantum Cosmology Mathematical Physics Mathematical Physics
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques
Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF.
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model
We prove that, under standard assumptions, SGD can converge even in this extremely loose, decentralized setting, for both convex and non-convex objectives.
Towards Understanding the Geometry of Knowledge Graph Embeddings
These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space.
Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images
Increasing demand for high field magnetic resonance (MR) scanner indicates the need for high-quality MR images for accurate medical diagnosis.
Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods
RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process.
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning
How much can pruning algorithms teach us about the fundamentals of learning representations in neural networks?
