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Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning.
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
kwea123/ngp_pl
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Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.
DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
We sampled, modified, and recorded 2, 541 dialogues from the open-domain dialogue dataset DailyDialog which are adequately long to represent context of each dialogue.
Back to MLP: A Simple Baseline for Human Motion Prediction
dulucas/simlpe
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This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences.
LViT: Language meets Vision Transformer in Medical Image Segmentation
huanglizi/lvit
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In our model, medical text annotation is introduced to compensate for the quality deficiency in image data.
Ranked #1 on
Medical Image Segmentation
on MoNuSeg
Disentangling Random and Cyclic Effects in Time-Lapse Sequences
harskish/tlgan
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We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal.
Text2Human: Text-Driven Controllable Human Image Generation
In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation.
BoT-SORT: Robust Associations Multi-Pedestrian Tracking
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object.
Ranked #1 on
Multi-Object Tracking
on MOT20
(using extra training data)
Forecasting Future World Events with Neural Networks
We test language models on our forecasting task and find that performance is far below a human expert baseline.
Ivy: Templated Deep Learning for Inter-Framework Portability
ivy-dl/ivy
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We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.
