A5057482475- Stochastic Gradient Optimization Techniques
- Ruminant Nutrition and Digestive Physiology
- Advanced Neural Network Applications
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Data Classification
- Evolutionary Psychology and Human Behavior
- Data Stream Mining Techniques
- Language, Metaphor, and Cognition
- Milk Quality and Mastitis in Dairy Cows
- Machine Learning and ELM
- Humor Studies and Applications
- Genetic and phenotypic traits in livestock
- Computational Physics and Python Applications
- Face Recognition and Perception
- Mobile Crowdsensing and Crowdsourcing
- Robotics and Automated Systems
- Advanced Bandit Algorithms Research
- Hand Gesture Recognition Systems
- Turfgrass Adaptation and Management
- Data Visualization and Analytics
- Hearing Impairment and Communication
- Digital Communication and Language
- RNA and protein synthesis mechanisms
- Blind Source Separation Techniques
- Misinformation and Its Impacts
University of Wisconsin–Madison
1993-2023
United States Air Force Research Laboratory
2022
University of Minnesota
2015
<ns3:p>The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. provides implementations of probabilistic k-mer counting data structure, compressible De Bruijn graph representation, partitioning, and digital normalization. implemented in C++ Python, under the BSD license at <ns3:ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri"...
Altair is a declarative statistical visualization library for Python.Statistical constrained subset of data focused on the creation visualizations that are helpful in modeling.The model usually expressed terms grammar (Wilkinson, 2005) specifies how input transformed and mapped to visual properties (position, color, size, etc.).
Distributed model training suffers from communication overheads due to frequent gradient updates transmitted between compute nodes. To mitigate these overheads, several studies propose the use of sparsified stochastic gradients. We argue that are facets a general sparsification method can operate on any possible atomic decomposition. Notable examples include element-wise, singular value, and Fourier decompositions. present ATOMO, framework for Given gradient, an decomposition, sparsity...
Eight multiparous, ruminally cannulated Holstein cows averaging 40 d in milk and 575 kg BW at the start of trial were a replicated 4 × Latin square arrangement (28-d periods) to determine effects dietary nonfiber carbohydrate (NFC) level Aspergillus oryzae fermentation extract (AO) on intake, production, nutrient digestibility. Treatments 42 or 35% NFC 0 3 g AO per day arranged as 2 factorial. Diets formulated contain 21% NDF from alfalfa silage (48.4% ration DM) 18.5% CP fed total mixed...
Nearly every machine learning model requires hyperparameters, parameters that the user must specify before training begins and influence performance. Finding optimal set of hyperparameters is often a time- resource-consuming process. A recent breakthrough hyperparameter optimization algorithm, Hyperband finds high performing with minimal via principled early stopping scheme for random selection li2016hyperband. This paper will provide an intuitive introduction to explain implementation in...
Obtaining useful crowdsourcing results often requires more responses than can be easily collected. Reducing the number of required done by adapting to previous with \textquotedbl{}adaptive\textquotedbl{} sampling algorithms, but these algorithms present a fundamental challenge when paired crowdsourcing. At UWâMadison, we have built powerful data collection tool called NEXT (http://nextml.org) that used arbitrary adaptive algorithms. Each week, our system is The New Yorker run their Cartoon...
Machine learning (ML) relies on stochastic algorithms, all of which rely gradient approximations with \textquotedbl{}batch size\textquotedbl{} examples. Growing the batch size as optimization proceeds is a simple and usable method to reduce training time, provided that number workers grows size. In this work, we provide package trains PyTorch models Dask clusters, can grow if desired. Our simulations indicate for particular model uses GPUs popular image classification task, time be reduced...
Social scientists often investigate human reasoning by collecting relative similarity judgements with crowdsourcing services.However, this requires too many responses to be practical for large experiments.To address problem, we introduce software called Salmon, which makes intelligent choices on query selection (aka active machine learning or adaptive sampling) while judgments from participants.Salmon is usable experimentalists because it little no programming experience and only an Amazon...
Mini-batch stochastic gradient descent (SGD) and variants thereof approximate the objective function's with a small number of training examples, aka batch size. Small sizes require little computation for each model update but can yield high-variance estimates, which poses some challenges optimization. Conversely, large batches more higher precision estimates. This work presents method to adapt size model's loss. For various function classes, we show that our requires same order updates as...
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 captions, collected through crowdsourcing rating data The New Yorker's weekly cartoon caption contest the past eight years. This unique supports development and evaluation large language models preference-based fine-tuning algorithms humorous generation. propose benchmarks judging quality model-generated utilizing both GPT4 judgments to establish ranking-based...
Classifying whether collected information related to emerging topics and domains is fake/incorrect not an easy task because we do have enough labeled data in the domains. Given from source (e.g., gossip health) limited a newly target domain COVID-19 Ukraine war), simply applying knowledge learned may work well of different distribution. To solve problem, this paper, propose energy-based adaptation with active learning for early misinformation detection. three real world news datasets,...
The present work advances the science of smile by investigating how perceivers mentally represent this heterogenous expression. Across both perception- and production-based tasks, we report evidence that reward, affiliation, dominance smiles as distinct categories associated with specific behaviors, social contexts, facial movements. Study 1 demonstrates expect to behave differently in response each type when embedded a simulated economic game. 2 use words describe contexts which they...