Philosophy
116 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
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
ZhaofanQiu/pseudo-3d-residual-networks
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ICCV 2017
In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating $3\times3\times3$ convolutions with $1\times3\times3$ convolutional filters on spatial domain (equivalent to 2D CNN) plus $3\times1\times1$ convolutions to construct temporal connections on adjacent feature maps in time.
AXNet: ApproXimate computing using an end-to-end trainable neural network
To guarantee the approximation quality, existing works deploy two neural networks (NNs), e. g., an approximator and a predictor.
Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains
We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings.
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
PaddlePaddle/PaddleSpeech
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NAACL (ACL) 2022
PaddleSpeech is an open-source all-in-one speech toolkit.
FlowX: Towards Explainable Graph Neural Networks via Message Flows
divelab/DIG
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We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms.
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
zhejz/hptr
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In this paper, we present Wayformer, a family of attention based architectures for motion forecasting that are simple and homogeneous.
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
mlsysops/active-learning-as-a-service
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In data-centric AI, active learning (AL) plays a vital role, but current AL tools 1) require users to manually select AL strategies, and 2) can not perform AL tasks efficiently.
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields.
