DUCKQUANT / THE QUANT & AI DIRECTORY
2,336 resources, ranked by in-directory PageRank
Publication: Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. Published Jan 28, 2019.
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Alternative data research, sentiment factor modeling. Free tier: 250 requests/month, 30 days of history, non-commercial
Official Repository for QuantHarness
Yahoo China - Alibaba Group - Zhikuang(Quantopian-like startup) - RightCapital Inc. - rainx
AI-powered fraud detection for financial institutions.
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Publication: Retraining-Based Iterative Weight Quantization for Deep Neural Networks. Published May 29, 2018.
Publication: Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning. Published Nov 16, 2016.
Publication: And the Bit Goes Down: Revisiting the Quantization of Neural Networks. Published Jul 12, 2019.
Publication: Training Deep Neural Networks with 8-bit Floating Point Numbers. Published Dec 19, 2018.
Publication: Scalable Methods for 8-bit Training of Neural Networks. Published May 25, 2018.
Publication: Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. Published Dec 15, 2017.
Publication: Learning low-precision neural networks without Straight-Through Estimator(STE). Published Mar 4, 2019.
Publication: Deep Learning with Low Precision by Half-wave Gaussian Quantization. Published Feb 3, 2017.
Publication: Least squares binary quantization of neural networks. Published Jan 9, 2020.
Publication: Towards Accurate Binary Convolutional Neural Network. Published Nov 30, 2017.
This paper presents a pruning method, Reconstruction Error Aware Pruning (REAP), to reduce the redundancy of convolutional neural network models for accelerating the inference.…
Band-limited Training and Inference for Convolutional Neural NetworksAdam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Micha...
Publication: EPNAS: Efficient Progressive Neural Architecture Search. Published Jul 7, 2019.
In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of…
Publication: Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. Published Aug 10, 2019.
Publication: PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration. Published Jan 26, 2019.
Publication: AutoQ: Automated Kernel-Wise Neural Network Quantization. Published Feb 15, 2019.
Publication: Neural Network Pruning with Residual-Connections and Limited-Data. Published Nov 19, 2019.
Publication: BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. Published Oct 25, 2019.
Efficient deep learning inference requires algorithm and hardware codesign to enable specialization: we usually need to change the algorithm to reduce memory fo
Authors: Weixian Waylon Li, Hyeonjun Kim, Mihai Cucuringu, Tiejun Ma. Large Language Models (LLMs) have recently been leveraged for asset pricing tasks and…
Explore this resource at its source website for documentation and current information.
Webfont containing icons for popular payment methods and card networks
A curated list of awesome resources for quantitative investment and trading strategies focusing on artificial intelligence and machine learning applications in…
An exporter capable of connecting to multiple exchanges and getting account balances and exchange rates
Extract geographic revenue breakdowns from SEC EDGAR 10-K filings with no API key
Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model
Events calendar that shows you upcoming events on different projects
Market sentiment and crypto coins tracker
