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Andrew Carl's Projects

adamw-and-sgdw icon adamw-and-sgdw

keras implementation of AdamW from Fixing Weight Decay Regularization in Adam (https://arxiv.org/abs/1711.05101)

adaptapprox icon adaptapprox

code for the paper "Adaptive classification for prediction under a budget", NIPS 2017

adaptive-forex-forecast icon adaptive-forex-forecast

An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms

adaptive-softmax icon adaptive-softmax

Implements an efficient softmax approximation as described in the paper "Efficient softmax approximation for GPUs" (http://arxiv.org/abs/1609.04309)

adaptive_kernel_methods icon adaptive_kernel_methods

Python code for Kernel LMS (KLMS) algorithm with sklearn API. We also implemented neural spike kernels. We also have KLMS and Kernel PCA for neural spike data.

adaptsegnet icon adaptsegnet

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

add-lyrics icon add-lyrics

A simple python program for adding lyrics to all of your music library

additive-gps icon additive-gps

Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.

addons icon addons

Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

ademxapp icon ademxapp

Code for https://arxiv.org/abs/1611.10080

adios icon adios

ADIOS: Architectures Deep In Output Space

adpac_wrapper icon adpac_wrapper

Component wrapper for ADPAC (Advanced Ducted Propfan Analysis Code)

adstar icon adstar

My Implementation of the Anytime D* in C++

adt_opt icon adt_opt

Trajectory optimization w/ draco act dynamics

adult-income-analysis icon adult-income-analysis

Exploratory data analysis for the Adult or Census Income dataset from UCI Machine Learning Repository : https://archive.ics.uci.edu/ml/datasets/adult

adv-alstm icon adv-alstm

Code for paper "Enhancing Stock Movement Prediction with Adversarial Training" IJCAI 2019

advanced-comp-2017 icon advanced-comp-2017

💻 Material for a course on applied machine-learning for scientists. Taught at EPFL in spring 2017

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