🚀 Tomorrow! Prof. Joel P. Arrais (Univ. of Coimbra) on how #AI & #GenerativeModels transform drug discovery—from genomics to molecule design. Join the online #SanoSeminar 👉
sano.science/seminars/generative-models-for-drug-discovery-from-pharmacogenomics-to-targeted-molecular-design/ #DrugDiscovery

Table 3: Category-wise evaluation scores. Likert scores are averaged over both annotators, correlation measured between individual Likert scores and percent agreement.

🔍🧠 Their experiments show that #LLMs can produce reasonable poem descriptions, but struggle with more abstract interpretion, highlighting where #NLG currently meets its #limits in #LiteraryInterpretation.

#LiteraryComputing #Evaluation #GenerativeModels

Deep learning in-depth analysis of crystal graph convolutional neural networks:

This review evaluates #CGCNN in #MaterialsInformatics, detailing architecture, limitations, and integration with #GenerativeModels, while outlining benchmarking and strategies to advance data‑driven #MaterialsDiscovery.

#OpenAccess in Nanotechnology Reviews: doi.org/10.1515/ntre...

SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows
Guangting Zheng, Liang Zheng et al.
Paper
Details
#LatentFlows #DeepLearning #GenerativeModels

#Fanvue #Creators using #aimodel, what applications did you use to create your models?

#aicreators #generativemodels #generativecreators

The HackerNoon Newsletter: AI Brawl: the Generative Model Showdown (11/9/2025) How are you, hacker? 🪐 What’s happening in tech today, November 9, 2025? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The chemical element Darmstadtium...

The HackerNoon Newsletter: AI Brawl: the Generative Model Showdown (11/9/2025) #Technology #EmergingTechnologies #ArtificialIntelligence #AI #GenerativeModels #TechnologyNews

AI can crunch the data, but humans still set the vision, keep the team vibe safe, and steer through uncertainty. Discover the 5% rule for mixing skillsets and why psychological safety matters in a data‑driven world. #AILeadership #GenerativeModels #MixedSkillWorkforce

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Profile HMMs and other Hidden Markov Models explained from we @weratedags.com at #bsky
#bioinformatics #generativemodels #probability #latentvariables #statistics

Profile HMMs and other Hidden Markov Models explained from we @weratedags.com at #bsky
#bioinformatics #generativemodels #probability #latentvariables #statistics

Profile HMMs and other Hidden Markov Models explained from we @weratedags.com at #bsky
#bioinformatics #generativemodels #probability #latentvariables #statistics

Qualitative Comparison of Cognitive and Generative AI Theories

A study compares cognitive architectures and generative models, noting cognitive systems favor interpretability while generative models excel in flexibility. getnews.me/qualitative-comparison-o... #cognitivearchitectures #generativemodels #ai

Conditional Generative Model Identification Improves Search via Images

CGI lets users upload example images and correctly identifies the right generative AI model 92% of the time with just four images in a benchmark of 65 models. Read more: getnews.me/conditional-generative-m... #cgimethod #generativemodels

Theoretical Insights into Discrete Flow Matching Generative Models

Researchers prove Discrete Flow Matching models converge to true data distribution, tying total variation error to learned velocity field risk (Sep 26, 2025). Read more: getnews.me/theoretical-insights-int... #discreteflowmatching #generativemodels

Stochastic Interpolation Shows Generative Model Limits with Small Data

Deterministic models exactly reproduce training samples; stochastic version adds Gaussian noise to create perturbed copies, balancing memorization and variation on limited data. getnews.me/stochastic-interpolation... #generativemodels #ai

DS-Diffusion Introduces Style-Guided Generation for Time-Series Data

DS‑Diffusion reduces predictive score by 5.56% and discriminative score by 61.55% versus ImagenTime, while eliminating conditional retraining. Read more: getnews.me/ds-diffusion-introduces-... #dsdiffusion #timeseries #generativemodels

Generative Test for Conditional Distribution Equality Minimax

A generative framework tests equality of conditional distributions by sample splitting, using permutation‑based and classification tests with minimax guarantees. Read more: getnews.me/generative-test-for-cond... #conditionaltesting #generativemodels

TIMED Advances AI-Driven Synthetic Time Series Generation

TIMED combines diffusion, autoregressive supervision and a Wasserstein adversarial critic, beating prior models on multivariate benchmarks and accepted for ICDM 2025. Read more: getnews.me/timed-advances-ai-driven... #synthetictimeseries #generativemodels

ICCS is exploring #GenerativeModels (Diffusion, GANs) to boost AI robustness in adversarial settings.
By generating adversarial examples & defenses, we aim to enhance ML 🔒 security, ✅ reliability & 💪 resilience in real-world applications.

#AI #Cybersecurity #ML #EUFunded

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for
Reinforcement Learning under a Generative Model
Andris Ambainis, Debbie Lim et al.
Paper
Details
#ReinforcementLearning #GenerativeModels #QuantumAlgorithms

#AI #Weather #GenerativeModels @theturing.bsky.social

Automating Handwritten Vaccination Record Transcription with Generative Multimodal AI Models: A Proof of Concept Study from The Gambia
Burstein, R., Danovaro-Holliday, M. C. et al.
Paper
Details
#AIinHealthcare #VaccinationRecords #GenerativeModels

Are you interested in Invertible Convolution or Flow models?
I will be presenting our work 'Inverse-Flow' at #AISTATS25
Session 3, 5th may, 3-6 pm, Hall A-E 48.
#GenerativeModels , #ML, #phuket

Another year, here we are!!! At #Evostar presenting our work on #GenerativeModels and #EvolutionaryComputing. Thanks to #SPECIESsociety for organizing.
The research was carried out with @francischicano.bsky.social at ITIS Software @univmalaga.bsky.social

Check out these two great courses on Diffusion and Flow models! 📚🌀
1. www.youtube.com/watch?v=8mxC...
2. www.youtube.com/watch?v=GCoP...

#GenerativeModels, #Diffusion, #Flow, #GenAI, #MachineLearning, #CompterVision, #ML

Exploring the intersection of Diffusion and Flow Matching models! 🚀 1. Flow models intro. 2. Clears up the confusion, showing how Gaussian flow matching and diffusion models align:
1. 🔗 mlg.eng.cam.ac.uk/blog/2024/01...
2. 🔗 diffusionflow.github.io
#FlowMatching #DiffusionModels #GenerativeModels

ViTok’s Scalable Design Boosts AI Efficiency in Image and Video Processing Researchers introduce ViTok, a Vision Transformer-based auto-encoder that scales visual tokenization to enhance image and video generation while reducing computational costs.

ViTok’s Scalable Design Boosts AI Efficiency in Image and Video Processing 🚀📸🎥 www.azoai.com/news/2025011... #AI #MachineLearning #ComputerVision #AutoEncoding #DeepLearning #Transformers #ImageProcessing #VideoAI #GenerativeModels #TechResearch

Cracks in the Code: Why AI Struggles to Build Coherent Worlds Large language models struggle with coherence in tasks like navigation and logic puzzles, as new metrics expose gaps in their ability to recover underlying world models.

Cracks in the Code: Why AI Struggles to Build Coherent Worlds 🚀📊🧠 www.azoai.com/news/2025010... #AI #LanguageModels #WorldModels #MachineLearning #Navigation #LogicPuzzles #AIResearch #GenerativeModels #TechInnovation #AIInsights @arxiv-stat-ml.bsky.social

GPD-1: The Next Leap in Autonomous Driving Technology Explore GPD-1's transformative approach to motion planning and traffic simulation for smarter vehicles.

GPD-1: The Next Leap in Autonomous Driving Technology 🚗🤖✨ www.azoai.com/news/2025010... #AutonomousDriving #AI #MachineLearning #GenerativeModels #TrafficSimulation #MotionPlanning #SmartTechnology #FutureMobility #AI #Research @arxiv-stat-ml.bsky.social

Unificazione ed efficienza: Il Training-Free Guidance (TFG) nei Modelli Generativi Il Training-Free Guidance (TFG) semplifica la generazione condizionale eliminando il riaddestramento. Utilizzando predictor preaddestrati, come classificatori o funzioni di perdita, guida il processo senza sacrificare qualità. Unifica tecniche esistenti, ottimizza iperparametri ed è applicabile in contesti vari, dalle immagini alle molecole e all'audio. Mitiga bias nei dataset, riduce costi e tempi, e favorisce applicazioni scalabili, flessibili e inclusive.

𝐔𝐧𝐢𝐟𝐢𝐜𝐚𝐳𝐢𝐨𝐧𝐞, 𝐟𝐥𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐚̀ 𝐞 𝐯𝐢𝐬𝐢𝐨𝐧𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜𝐚: 𝐢𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠-𝐅𝐫𝐞𝐞 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞 (𝐓𝐅𝐆) 𝐞 𝐢𝐥 𝐟𝐮𝐭𝐮𝐫𝐨 𝐝𝐞𝐥𝐥𝐚 𝐠𝐞𝐧𝐞𝐫𝐚𝐳𝐢𝐨𝐧𝐞 𝐜𝐨𝐧𝐝𝐢𝐳𝐢𝐨𝐧𝐚𝐭𝐚

#GenerativeModels #Innovation #ArtificialIntelligence #AiGenerated #TFG

www.andreaviliotti.it/post/unifica...