Search Results for author:
Found 15 papers, 8 papers with code
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
Personalized Large Language Models
Large language models (LLMs) have significantly advanced Natural Language Processing (NLP) tasks in recent years.
Into the Unknown: Self-Learning Large Language Models
We introduce a concept called Point in the Unknown (PiU) to identify atomic knowledge unknown to a model, along with four methods for automatic PiUs identification, facilitating the creation of a self-learning loop that focuses exclusively on the absorption of currently unknown knowledge into the model.
From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection
It seems that concatenating personalized datasets, even with the cost of normalizing the range of annotations across all datasets, if combined with the personalized models, results in an enormous increase in the performance of humor detection.
Towards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems
Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model.
Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows
It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits additional information about the reader to make more accurate predictions.
Scaling Representation Learning from Ubiquitous ECG with State-Space Models
We train this model in a self-supervised manner with 275, 000 10s ECG recordings collected in the wild and evaluate it on a range of downstream tasks.
Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress
Wearables like smartwatches or wrist bands equipped with pervasive sensors enable us to monitor our physiological signals.
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings
Recently, a variety of model designs and methods have blossomed in the context of the sentiment analysis domain.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Extracting Aspects Hierarchies using Rhetorical Structure Theory
We propose a novel approach to generate aspect hierarchies that proved to be consistently correct compared with human-generated hierarchies.
Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRF
We proposed a~new accurate aspect extraction method that makes use of both word and character-based embeddings.
Using Machine Learning to Predict the Evolution of Physics Research
The advancement of science as outlined by Popper and Kuhn is largely qualitative, but with bibliometric data it is possible and desirable to develop a quantitative picture of scientific progress.
WordNet2Vec: Corpora Agnostic Word Vectorization Method
Since WordNet embeds natural language in the form of a complex network, a transformation mechanism WordNet2Vec is proposed in the paper.
Learning in Unlabeled Networks - An Active Learning and Inference Approach
The question that arises is: "labels of which nodes should be collected and used for learning in order to provide the best classification accuracy for the whole network?".
