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Google TrendANNA BONDAR (200+) — October 12, 2026: Tommy Paul vs Valentin Vacherot: Set 2 Winner Tennis Prediction Market
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Category:All HeadlinesWorld & GeopoliticsMarkets & EconomyTech & AIPoliticsSearch results for: "Sequence modeling and des" (30 stories)

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Next-Gen Restaurant Recommendation with Generative Modeling and Real-Time Features
Lead StoryUbergeneral
Apr 16

Next-Gen Restaurant Recommendation with Generative Modeling and Real-Time Features

<a href="https://news.google.com/rss/articles/CBMiekFVX3lxTE5JRV9WVnZTQjBpc3ljdTNTUmttVzY1cHIxakVyNXZhUUFEejctTlpYNUd5RWUtZl9ZMHU2WVBldFpwV2dZZEhySVBfYTJ2VmtVQUhsQ0pldUpTcnYzUzJtbU80M0FUbEd0MmJ2NWFMLVNDSkhVT2s4bWlB?oc=5" target="_blank">Next-Gen Restaurant Recommendation with Generative Modeling and Real-Time Features</a>  <font color="#6f6f6f">Uber</font>

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Search Results for "Sequence modeling and des"

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29 stories displayed
Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits
Naturetech

Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE80ZmJlc2ZKdVYtRFR5dERxS29tc2RWSFdoSVduWlR1dDFFaFZ2Um4tODZlVW9YUW1sN1FYc0tET1c4RDQwUVRPYUMxSFZ3RnV3YjVoWVR3WGRIcE9vVGxR?oc=5" target="_blank">Genome-wide modelling of plant transcription factor binding captures regulatory variants associated with phenotypic traits</a>  <font color="#6f6f6f">Nature</font>

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Zyphra Introduces Tensor and Sequence Parallelism (TSP): A Hardware-Aware Training and Inference Strategy That Delivers 2.6x Throughput Over Matched TP+SP Baselines
MarkTechPosttech

Zyphra Introduces Tensor and Sequence Parallelism (TSP): A Hardware-Aware Training and Inference Strategy That Delivers 2.6x Throughput Over Matched TP+SP Baselines

<a href="https://news.google.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?oc=5" target="_blank">Zyphra Introduces Tensor and Sequence Parallelism (TSP): A Hardware-Aware Training and Inference Strategy That Delivers 2.6x Throughput Over Matched TP+SP Baselines</a>  <font color="#6f6f6f">MarkTechPost</font>

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Titans + MIRAS: Helping AI have long-term memory
Google Researchtech

Titans + MIRAS: Helping AI have long-term memory

<a href="https://news.google.com/rss/articles/CBMigAFBVV95cUxQT09kQldONHlTQlJsNmJTTzRRRWowZVBpaWN0YVJkSkJ5NUtGYmlhcF9tRzg4aEJTQXJZZU51czlFN2xoOUpmMGx6QzY4WU9FZTR2SWl3WXhLRmtmVFRsSjVxUnhHd2N2TTVKek1tTmprdW01VnVpZGY0U3JOZXNuOQ?oc=5" target="_blank">Titans + MIRAS: Helping AI have long-term memory</a>  <font color="#6f6f6f">Google Research</font>

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Connecting the dots: deep learning-based automated model building methods in cryo-EM
Frontiersgeneral

Connecting the dots: deep learning-based automated model building methods in cryo-EM

<a href="https://news.google.com/rss/articles/CBMioAFBVV95cUxOd1BUUDl3NXV1VVF2WnA3ZUprVHdvM0xaSUZXb0ozcHlVVkpkam5GV0p3U2dSdms1WGEteExHeUQ3SDFYa2ZYeG1nb1d5Rkd3VDFkZzFFVTkyU1BZemNETDRiZC15Y0M4Q2ktcEZpLWlhLWZnN0lnTWhnWHhXQWhCbDF2Mno5OExPVnB2YUJXeXlhOVBaQ2NSSkxhVDQ1aDRm?oc=5" target="_blank">Connecting the dots: deep learning-based automated model building methods in cryo-EM</a>  <font color="#6f6f6f">Frontiers</font>

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Sequence learning: A paradigm shift for personalized ads recommendations
Engineering at Metageneral

Sequence learning: A paradigm shift for personalized ads recommendations

<a href="https://news.google.com/rss/articles/CBMirgFBVV95cUxNbFdhSFBNZ2Qxb1FnX2ZuZW5aTlFMUW1EcUo4dG1wMkxwQmZqM1ZuWlNuYXJYY0Q2cG0xcjZGQ0l2Z1JPTkZMWWRpVmJTRERrN0o5ZzZ3UTVGOWtIWE9xeW9RX1ZPTTE5RGxiWFd1MENFNy0tWFluMWhFcnBJa0FmMTc4SlNrem5aT0Y2VFpKUTNrTTh3cnFiWVQydEdxY2ZqaHBXNWhSVnlfeXRBU3c?oc=5" target="_blank">Sequence learning: A paradigm shift for personalized ads recommendations</a>  <font color="#6f6f6f">Engineering at Meta</font>

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Modeling evolutionary landscapes: Mutational stability, topology, and superfunnels in sequence space
PNASscience

Modeling evolutionary landscapes: Mutational stability, topology, and superfunnels in sequence space

<a href="https://news.google.com/rss/articles/CBMiXkFVX3lxTE01b1VHdFFvOHRxazJqaVo4cG53X3lwaHVycDJndXhzQmhJdjFrVUctYkdZRGJnOE9DbDA4a3FXMi1FZzJ0bV94MTd5blpRUE1lbU9VcGdyRFdOQ2lLZkE?oc=5" target="_blank">Modeling evolutionary landscapes: Mutational stability, topology, and superfunnels in sequence space</a>  <font color="#6f6f6f">PNAS</font>

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Retentive Network: A Successor to Transformer for Large Language Models
alphaXivgeneral

Retentive Network: A Successor to Transformer for Large Language Models

<a href="https://news.google.com/rss/articles/CBMiUEFVX3lxTE9lcG15VVI1RktZRDdKZ0lqSW0zNDRlY2hUTTlCUXhBcGY2UWk5bmVwazZ6UzhJNlBsMkxvOUtoY2k5R2NhZWFjNzhoREplQ3FE?oc=5" target="_blank">Retentive Network: A Successor to Transformer for Large Language Models</a>  <font color="#6f6f6f">alphaXiv</font>

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PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization
Wiley & Sonsworld

PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization

<a href="https://news.google.com/rss/articles/CBMidkFVX3lxTE01bmVacmQtM2JLcGl1WllDOWk1NmVaRFFyUm1UUTVtbWdoVXNTMUNhQzRBMlZtR1Q5ck56MEprbWxZN2pfbnRQU2w1TlMzVDh5VlpldnFqSGhUTk1BZWVTaExaeUtrSXJFX2NiaHRQTUZmSGZqeFE?oc=5" target="_blank">PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization</a>  <font color="#6f6f6f">Wiley & Sons</font>

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Machine tool FEM model correction assisted by dynamic evolution sequence
Naturegeneral

Machine tool FEM model correction assisted by dynamic evolution sequence

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9kbWJqQXc1cFdiRXpqUzZxaHBpMHVPWFRNal9RRmlES3VIbVVtVTVYX3FwakR4djcwZ0Y2ZUphM1Njako5T3BxUHNVYVdjUHI3SDBaWkpPNXVSMjh3UnU0?oc=5" target="_blank">Machine tool FEM model correction assisted by dynamic evolution sequence</a>  <font color="#6f6f6f">Nature</font>

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TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window
MarkTechPostgeneral

TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window

<a href="https://news.google.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?oc=5" target="_blank">TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding with only 7B Params with 256k Context Window</a>  <font color="#6f6f6f">MarkTechPost</font>

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EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
alphaXivworld

EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling

<a href="https://news.google.com/rss/articles/CBMiUEFVX3lxTFBob3pqRlhoNXNDdnFId2JCeTJmS3hPRi1pTVRUbEdRQjBwaDBRdWdXa2xCXzJCNHJVOG55d29RMDR3ME1tY0UwaXhYS285b2pV?oc=5" target="_blank">EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling</a>  <font color="#6f6f6f">alphaXiv</font>

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A brain-inspired sequence learning model based on a logic
Naturetech

A brain-inspired sequence learning model based on a logic

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE4ybm84MTYtek9oN19ONUttOHg4a2ZUOGJjeFI4aHlXOC1uTWZkZ1ZWeHFveHRKRlNKNUY1WE9nUGo0MkhPQ1d1eWQtTVh6YkM4bF9mOW03aU9yLThjYWVv?oc=5" target="_blank">A brain-inspired sequence learning model based on a logic</a>  <font color="#6f6f6f">Nature</font>

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The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry Between ML Tool Origins and Research Adoption
MarkTechPosttech

The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry Between ML Tool Origins and Research Adoption

<a href="https://news.google.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?oc=5" target="_blank">The Machine Learning Divide: Marktechpost’s Latest ML Global Impact Report Reveals Geographic Asymmetry Between ML Tool Origins and Research Adoption</a>  <font color="#6f6f6f">MarkTechPost</font>

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DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers
alphaXivworld

DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers

<a href="https://news.google.com/rss/articles/CBMiUEFVX3lxTFBzQ2ZIcXlZUDhSY3JWNDY3cFM1Mi1ieUhOYUkwU244U2RfTkxHdFJGSFFKV2JjSnQ1MnBKN1ZtWXJzMHpjQ2F0azFQMmR1NVpW?oc=5" target="_blank">DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers</a>  <font color="#6f6f6f">alphaXiv</font>

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From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2
Naturegeneral

From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE90T2k3R3lvQkM4QXFFNV90Rmc0cjZCQ1JxZFRKNHFKSEM3ZjFzeFNDUkt1dmJvUTBOc3VoU2F1b05Gd0MwWGNOTTVfYURscU5GYUYzNG9EbzdlLTA1TnJF?oc=5" target="_blank">From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2</a>  <font color="#6f6f6f">Nature</font>

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On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
alphaXivgeneral

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes

<a href="https://news.google.com/rss/articles/CBMiUEFVX3lxTE1uQXpsQXFDWlNKajhNMEFUVVhOR05nWVY1OUx5R001Yy1hdUtFTWVYRDg1YVFyQXRDZm5Ib1dGSUEtLWRkQWNMM2FfSzEtSklP?oc=5" target="_blank">On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes</a>  <font color="#6f6f6f">alphaXiv</font>

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Regression Transformer enables concurrent sequence regression and generation for molecular language modelling
Naturegeneral

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE85RTFRc1IwVzhPbUgzaW03VTBOWkdNMVpRMHZMZ0t4QlFuWkJlOTFDOVQwVmxCRkN0bXMyR0JEbWFBU3hDc2xQd1A2bENDVG5KS1NScm12RmdqbDZSOEZv?oc=5" target="_blank">Regression Transformer enables concurrent sequence regression and generation for molecular language modelling</a>  <font color="#6f6f6f">Nature</font>

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Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents
Naturegeneral

Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9MSEpNY1JWcVRQMGZlTVh3aC13dDY2bGVVd2FHVU9ZcjIyWnNwaXhqSUIwNndzMFNEWGhpeXpiS0xsTjQteVlPLWZBa2NnWE9OeXpzX1VSQzZ2Ql9QS0dr?oc=5" target="_blank">Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents</a>  <font color="#6f6f6f">Nature</font>

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Explainable sequence-to-sequence GRU neural network for pollution forecasting
Naturetech

Explainable sequence-to-sequence GRU neural network for pollution forecasting

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9uVmtWV19EaWl0ZjJfcm9FdE5VRHF3UXdJQmpFaVdmYzlGMFJTUFBxMkxpeE9za3VXdW1nZzRyVDBqbi1yMlp2MFJRc0NpZ0ROdnVmenBWOTRINXpiOXJr?oc=5" target="_blank">Explainable sequence-to-sequence GRU neural network for pollution forecasting</a>  <font color="#6f6f6f">Nature</font>

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PLMSearch: Protein language model powers accurate and fast sequence search for remote homology
Naturegeneral

PLMSearch: Protein language model powers accurate and fast sequence search for remote homology

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBsUklMa2NiaHFNbnRIa0xWQTNLXzhnN1lHYVBmQ3RlNE84N2JjZWZwQ0xFNktqZFI2dDZEMHZZeVBzdFhOX0w5RE12ZnQwX1dnYnZ4QzdTOGZ6TFdjYjFF?oc=5" target="_blank">PLMSearch: Protein language model powers accurate and fast sequence search for remote homology</a>  <font color="#6f6f6f">Nature</font>

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Neural networks to learn protein sequence–function relationships from deep mutational scanning data
PNASworld

Neural networks to learn protein sequence–function relationships from deep mutational scanning data

<a href="https://news.google.com/rss/articles/CBMiXEFVX3lxTE14cmZna1pLX3liTThnei1wUHR2R24wTEdzVXhxWEpjbjhFNlNXZXRCdFF4aEdkazFoWS01LV9aOUJMRnFjTHR3ZTB5LTBMUTBLR3pLT0JZR0R3Yllm?oc=5" target="_blank">Neural networks to learn protein sequence–function relationships from deep mutational scanning data</a>  <font color="#6f6f6f">PNAS</font>

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Parallelizing Linear Transformers with the Delta Rule over Sequence Length
alphaXivgeneral

Parallelizing Linear Transformers with the Delta Rule over Sequence Length

<a href="https://news.google.com/rss/articles/CBMiUEFVX3lxTFBpOXNIUjRrbDRoaTB3RVA2Z29BaEdVTjhIcGVIcTM0aER2b0h4QTF0X0F3RXlpanItczZWZm9OMk5NZVA3NkZ1WEIycm54R3Ro?oc=5" target="_blank">Parallelizing Linear Transformers with the Delta Rule over Sequence Length</a>  <font color="#6f6f6f">alphaXiv</font>

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What is a Transformer Model?
IBMgeneral

What is a Transformer Model?

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBQMmxPdmlucTE5clNETkgxOXdVMnZhbnMtQ2VoanlvY0RIV2JnMDFOUExjckJoQWxNNUlTMzZQR3Y0LUN3dzJXZmVMVGxWc2pMYnNRSXZ2X1dlN3lvd3U4?oc=5" target="_blank">What is a Transformer Model?</a>  <font color="#6f6f6f">IBM</font>

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ProteinGLUE multi-task benchmark suite for self-supervised protein modeling
Naturegeneral

ProteinGLUE multi-task benchmark suite for self-supervised protein modeling

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9fRnZYZ0s0Y2hzS1hiakwwd05NMW92M3dLU3l6a3Y2bHFQZWNTQmVVODlGWVRleFFZbFlNbl9malQ5b2x2ZjUwNi1uQkUxaEgxSjZyQXNwUHZ3OGhSVTVv?oc=5" target="_blank">ProteinGLUE multi-task benchmark suite for self-supervised protein modeling</a>  <font color="#6f6f6f">Nature</font>

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Protein sequence design with a learned potential
Naturegeneral

Protein sequence design with a learned potential

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE41LU9rYWVnWENRMkpuSkloSjRZdF9ERHgwQWdKQnpBeFVPV3pQVU5tVEdlbXhtYklGZnU4TFF0OEFTSFFJckFKMWVhVjh2ZjZuZjhwNTRQVUw5YnpDM0RV?oc=5" target="_blank">Protein sequence design with a learned potential</a>  <font color="#6f6f6f">Nature</font>

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Protein design and variant prediction using autoregressive generative models
Naturegeneral

Protein design and variant prediction using autoregressive generative models

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5henVuNVQyOEViaDlpQlVhdVlqMGc2QWFrUVZyc0hoVktQT0pXRFZJcjV2a2F5TUpOYmNJMU0yMUNhV1JwejhJLUY0cURZTUxHN3A4dlpVS25mQW1NUFJR?oc=5" target="_blank">Protein design and variant prediction using autoregressive generative models</a>  <font color="#6f6f6f">Nature</font>

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Bootstrapping Sign Language Annotations with Sign Language Models
Apple Machine Learning Researchgeneral

Bootstrapping Sign Language Annotations with Sign Language Models

<a href="https://news.google.com/rss/articles/CBMid0FVX3lxTFAxR2lfZFJXcXlhckhwTlMzTjJmVnpSazRIc0JBaEpFSW1DVmx6Y2dvaFU1OUhjbWFjU0RDd29mTWlrdlN6QktsbnUyZFBaaTFxLUh6SmFodDh5WE5WME5SeVhSVC13THAxdXVma3NuNzZFaW10WjZv?oc=5" target="_blank">Bootstrapping Sign Language Annotations with Sign Language Models</a>  <font color="#6f6f6f">Apple Machine Learning Research</font>

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Cell-type-directed design of synthetic enhancers
Naturegeneral

Cell-type-directed design of synthetic enhancers

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1QeS1MSm5xbHd5VFdic1p6c0FQZEJ5MTFCWEJEX1YwWDZfSGh4S3VYY19aeUJJY1FhOUxwWVlTLXZWZDJiSWdpS1Z0SlZXWm5lX19ZaW5pNFdyU0xCUDd3?oc=5" target="_blank">Cell-type-directed design of synthetic enhancers</a>  <font color="#6f6f6f">Nature</font>

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Inverse Text Normalization as a Labeling Problem
Apple Machine Learning Researchgeneral

Inverse Text Normalization as a Labeling Problem

<a href="https://news.google.com/rss/articles/CBMib0FVX3lxTFBBZE9SMHZtbWcwdFFlb3BJVGNVejV6RHgxdUducmZLUmV3WDd0QnlRVlQ3ZW10eXZKWERqUTFoMUVSTjlobS1jRU5kTWR6dEJsOW5JUndCV1h6WWQyRnBaUXNELWhMd0lmNDA3TzM1UQ?oc=5" target="_blank">Inverse Text Normalization as a Labeling Problem</a>  <font color="#6f6f6f">Apple Machine Learning Research</font>

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