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Found 12 papers, 6 papers with code
Towards a contextualised spatial-diachronic history of literature: mapping emotional representations of the city and the country in Polish fiction from 1864 to 1939
In this article, we discuss the conditions surrounding the building of historical and literary corpora.
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.
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.
From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis
In the era of artificial intelligence, data is gold but costly to annotate.
High-performance deep spiking neural networks with 0.3 spikes per neuron
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains.
ChatGPT: Jack of all trades, master of none
Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation.
An Exact Mapping From ReLU Networks to Spiking Neural Networks
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence.
On the visual analytic intelligence of neural networks
Visual oddity task was conceived as a universal ethnic-independent analytic intelligence test for humans.
Towards efficient end-to-end speech recognition with biologically-inspired neural networks
Automatic speech recognition (ASR) is a capability which enables a program to process human speech into a written form.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Online Spatio-Temporal Learning in Deep Neural Networks
For shallow networks, OSTL is gradient-equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients.
Deep learning incorporating biologically-inspired neural dynamics
Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in deep learning applied to Artificial Neural Networks (ANNs).
