Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR - Hands-On Workshop
Explore machine learning for ionic conductivity prediction using Schrödinger's AutoQSAR tool. Gain hands-on experience with MS Maestro interface to create and implement models for materials science applications.
Data-Driven Materials Innovation: Where Machine Learning Meets Physics
Explore how machine learning and physics-based methods synergize to revolutionize materials science, addressing challenges in data-driven design across various technology fields with Schrödinger's innovative tools.
Linear Structures in Transformer Embedding Spaces
Explore linear structures in Transformer embedding spaces, examining anisotropy, BERT's next sentence prediction, layer behavior, and multi-head attention modules.
Multilabel Classification Framework for Approximate Nearest Neighbor Search
Explore multilabel classification for approximate nearest neighbor search, enhancing space partitioning trees and enabling supervised classifiers as index structures for ANN search.
Automatic Speaking Assessment Systems for Second Language Learner's Spontaneous Speech in Under-Resourced Languages
Explore automatic speaking assessment for under-resourced languages, focusing on Finnish and Finland Swedish. Learn about system components, evaluation methods, and personalized feedback for language learners.
New Frontiers for AI-assisted Design and Discovery in Molecular Generation
Explore AI-assisted design for generating molecules with desired properties. Learn about overcoming challenges in combinatorial search, modeling interactions, and segregating representations for improved drug and material discovery.
Comparing Representations Learned by Neural Networks - Deconfounded Similarity Measures
Explore improved methods for comparing neural network representations, addressing data structure confounding and enhancing functional similarity identification in transfer learning and out-of-distribution scenarios.
Explainable and Robust AI for VILMA Virtual Laboratory
Explore explainable AI and uncertainty quantification in atmospheric research. Learn about SLISEMAP, a supervised manifold visualization method for local explanations and dimensionality reduction.
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Explore Gaussian pre-activations in neural networks, challenging common assumptions and proposing new initialization methods for improved information propagation in deep, narrow architectures.
Challenges in ML Engineering
Explore challenges in ML engineering, including cost-efficient inference, automation, accuracy monitoring, and anomaly detection for real-world AI applications.
New Frontiers for AI-assisted Design and Discovery in Molecular Generation
Explore AI-assisted design and discovery for generating molecules with desirable properties. Learn about overcoming challenges in combinatorial space, modeling interactions, and segregating representations for improved drug and material development.
The Use of Machine Learning in Computational Metabolomics Workflows
Explore machine learning applications in computational metabolomics for metabolite structure analysis. Learn about tools like MS2LDA, MotifDB, Spec2Vec, and MS2DeepScore for improved metabolite discovery and characterization.
Hierarchical Imitation Learning with Vector Quantized Models
Learn hierarchical imitation learning techniques using vector quantized models for complex decision-making tasks, exploring subgoal identification and planning strategies to outperform expert demonstrations.
Elucidating the Design Space of Diffusion-Based Generative Models
Explore advancements in diffusion-based generative models, including improved sampling, training, and score network preconditioning. Learn about state-of-the-art results and modular design changes for enhanced efficiency and quality.
Total Variation Minimization Perspective on Trustworthy Federated Learning
Explore total variation minimization in federated learning, covering computational and statistical aspects for designing trustworthy systems across various FL flavors.
Scalable and Flexible Distributed Reinforcement Learning Systems
Explore scalable distributed reinforcement learning systems, focusing on flexible training loops, statement management, and GPU allocation. Gain insights into building efficient RLHF systems for large-scale applications.
The Role of Machine Learning in Facilitating Fusion Energy Research
Explore machine learning applications in fusion energy research, including turbulence simulation speed-ups and predicting rare events. Gain insights into ML's potential to accelerate progress towards clean, abundant fusion energy.
Fast and Accurate Probabilistic Time Series Classification
Explore fast and accurate probabilistic time series classification using MINIROCKET technology, achieving near-state-of-the-art performance with significantly reduced computational costs compared to existing methods.
Next-Generation Networks for Machine Learning
Explore next-gen networks for machine learning, focusing on optical fabrics and scheduling techniques to accelerate distributed DNN training workloads and overcome datacenter network bottlenecks.
Efficient AI: From Supercomputers to Smartphones
Explore techniques for efficient AI training and inference, from data ingestion to model compression, enabling powerful models on supercomputers and smartphones.
HPC and AI/ML: A Synergistic Relationship
Explore synergies between HPC and AI/ML, focusing on scalable algorithms, efficient resource use, and data-driven models for performance optimization in large-scale computing environments.