Top Tweets for #classifier
Portable 2-deck vibrating screen classifier. New condition Invertek Optidrive vibration speed controller. Mounted on wheels. https://t.co/WFxEoIcwq9 #portable #vibratingscreen #portablescreen #classifier
New surplus Krebs gMAX20 (hydro)cyclone. 8in dia feed, 10in dia overflow, 8in dia underflow. 35in long cone. https://t.co/DvOVyC4h3e #cyclone #classifier #separation #mining
Back to the Basics: Revisiting the Median for Out-of-Distribution Detection from Unlabeled Data
https://t.co/z2MzHfAUsQ
#outliers #median #classifier
Cedarapids Greystone 36in dia by 25ft sandscrew. 8ft by 9ft 7in by 5ft 10in end hopper. Baldor Reliance 15HP 1765RPM electric motor. Dodge shaft mount 25:1 reducer. Trailer mounted w/ pintle hitch. https://t.co/woNTqHKACb #sandscrew #classifier #separator #dewatering
Anthropic introduceert auto mode voor Claude Code. Een classifier beslist welke acties veilig zijn. Nu als preview voor Team-abonnees. https://t.co/oTFMZeUcde #Devops #Anthropic #automode #classifier #ClaudeCode
Anthropic introduces auto mode for Claude Code. A classifier determines which actions are safe. Now available as a preview for Team subscribers. https://t.co/8KSmgngZuI #Devops #Anthropic #automode #classifier - Follow for more
Inverse classification with logistic and softmax classifiers: efficient optimization
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada.
Action editor: Dennis Wei.
https://t.co/dqtdTJmBmG
#classifiers #softmax #classifier
A Universal Source-Free Class Unlearning Framework via Synthetic Embeddings
https://t.co/DwfUr2HeDh
#unlearning #classifiers #classifier
Aluma's automatic tuning and continuous learning make it easy to keep a #classifier performing at a high level, even when documents change or new types come on-stream. https://t.co/IyUgrnmw7D #intelligentdocumentprocessing
'Learning Bayesian Network Classifiers to Minimize Class Variable Parameters', by Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno.
https://t.co/Pr4dGhsGEF
#classifier #bayesian #class
Ruby Classifier - Text Classification for Ruby (from 30/01/2026) #ruby #rubyonrails #programming #Classifier #Classification #classifier https://t.co/WKxdUqAAYt
Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions
https://t.co/TdlcdlthXq
#Predictomes #BioAI #classifier #AlphaFold
Summary: Protein–protein interactions (PPIs) are fundamental to nearly all cellular processes, yet we still lack a comprehensive structural map of how proteins physically interact across the proteome. While AlphaFold-Multimer (AF-M) offers a powerful tool for predicting the structures of protein complexes, its built-in confidence metrics are not sufficient to reliably distinguish true biological interactions from false positive predictions—especially in large-scale, proteome-wide screens.
To overcome this limitation, the authors developed SPOC (Structure Prediction and Omics-informed Classifier), a machine learning–based approach trained on curated datasets. SPOC integrates structural prediction features with omics data to effectively classify AF-M predicted PPIs as likely true or false. Importantly, it performs well even in large-scale, high-throughput prediction settings.
The researchers applied SPOC to an all-by-all interaction screen of nearly 300 human genome maintenance proteins, generating approximately 40,000 predicted protein complex structures. These predictions are publicly accessible at https://t.co/w6KFgEMd8n, where users can also evaluate their own AF-M predictions using the SPOC scoring framework.
High-confidence PPIs identified through this approach provide a valuable resource for hypothesis generation in genome maintenance research. More broadly, this work establishes a scalable framework for interpreting large-scale AlphaFold-Multimer screens and advances the goal of building a proteome-wide structural interactome.
Ruby Classifier - Text Classification for Ruby (from 30/01/2026) #ruby #rubyonrails #programming #Classifier #Classification #classifier https://t.co/WKxdUqAAYt
Unlocking [CLS] Features for Continual Post-Training
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Joaquin Vanschoren.
Action editor: Sarath Chandar.
https://t.co/gDZiMHgYtQ
#forgetting #classifier #adapting
Ruby Classifier - Text Classification for Ruby (from 30/01/2026) #ruby #rubyonrails #programming #Classifier #Classification #classifier https://t.co/WKxdUqAAYt
Automate land document organization with nuEra’s Classifier. The Land AI Assistant – Classifier helps organizations organize and categorize land documents with greater efficiency.
https://t.co/ZEtHn9ZDHQ
#LandData #LandAutomation #Document #Classifier
New #J2CCertification:
Classification of high-dimensional data with spiked covariance matrix structure
Yin-Jen Chen, Minh Tang
https://t.co/kqinFN1SCY
#classification #classifier #discriminant
Classification of high-dimensional data with spiked covariance matrix structure
Yin-Jen Chen, Minh Tang.
Action editor: Trevor Campbell.
https://t.co/UD1nE1dLqQ
#classification #classifier #discriminant
Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition
Barproda Halder, Faisal Hamman, Pasan Dissanayake, Qiuyi Zhang, Ilia Sucholutsky, Sanghamitra Dutta
https://t.co/klWPE3Whip
#spuriousness #spurious #classifier
Supervised score aggregation for active anomaly detection
Kevin Bleakley, Martin Royer, Benjamin Auder.
Action editor: Philip Chan.
https://t.co/ecqaJYxTHe
#anomaly #anomalies #classifier
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