Philosophy
118 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Philosophy
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BIG-bench
Most implemented papers
JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT
joeynmt/joeynmt
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JoeyS2T is a JoeyNMT extension for speech-to-text tasks such as automatic speech recognition and end-to-end speech translation.
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting.
I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench
Do large language models (LLMs) exhibit any forms of awareness similar to humans?
Monitoring Term Drift Based on Semantic Consistency in an Evolving Vector Field
Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis.
A Strategy for an Uncompromising Incremental Learner
Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting.
Element-centric clustering comparison unifies overlaps and hierarchy
Clustering is one of the most universal approaches for understanding complex data.
Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling
As a practical implementation of this link between the two workflows, we present a python package 'pcit', which implements our novel multivariate and conditional independence tests, interfacing the supervised learning API of the scikit-learn package.
Rasa: Open Source Language Understanding and Dialogue Management
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software.
Towards an Understanding of Neural Networks in Natural-Image Spaces
Two major uncertainties, dataset bias and adversarial examples, prevail in state-of-the-art AI algorithms with deep neural networks.
Evolving Event-driven Programs with SignalGP
We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox.
