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- ...C SELECTION FROM AMONG MULTIPLE CANDIDATE GENERATIVE MODELS WITH DIFFERING COMPUTATIONAL EFFICIENCIES= ...C SELECTION FROM AMONG MULTIPLE CANDIDATE GENERATIVE MODELS WITH DIFFERING COMPUTATIONAL EFFICIENCIES - A simplified explanation of the abstract==6 KB (880 words) - 07:58, 19 September 2024
- ...C SELECTION FROM AMONG MULTIPLE CANDIDATE GENERATIVE MODELS WITH DIFFERING COMPUTATIONAL EFFICIENCIES= ...C SELECTION FROM AMONG MULTIPLE CANDIDATE GENERATIVE MODELS WITH DIFFERING COMPUTATIONAL EFFICIENCIES - A simplified explanation of the abstract==6 KB (880 words) - 11:15, 19 September 2024
- =Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency= ==Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency - A simplified explanation of the abstract==3 KB (468 words) - 17:46, 1 January 2024
- ...STEMS FOR REDUCING COMPUTATIONAL LOADS IN THE MASS EXECUTION OF ANALYTICAL MODELS USING SCALE-OUT COMPUTING= ...STEMS FOR REDUCING COMPUTATIONAL LOADS IN THE MASS EXECUTION OF ANALYTICAL MODELS USING SCALE-OUT COMPUTING - A simplified explanation of the abstract==2 KB (312 words) - 06:38, 10 November 2023
- ...TING CLINICAL EFFICACY OF MULTI-LABEL MULTI-CLASS COMPUTATIONAL DIAGNOSTIC MODELS= ...TING CLINICAL EFFICACY OF MULTI-LABEL MULTI-CLASS COMPUTATIONAL DIAGNOSTIC MODELS - A simplified explanation of the abstract==5 KB (701 words) - 10:20, 25 March 2024
Page text matches
- =TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS= ==TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS - A simplified explanation of the abstract==3 KB (460 words) - 08:01, 12 July 2024
- ...STEMS FOR REDUCING COMPUTATIONAL LOADS IN THE MASS EXECUTION OF ANALYTICAL MODELS USING SCALE-OUT COMPUTING= ...STEMS FOR REDUCING COMPUTATIONAL LOADS IN THE MASS EXECUTION OF ANALYTICAL MODELS USING SCALE-OUT COMPUTING - A simplified explanation of the abstract==2 KB (312 words) - 06:38, 10 November 2023
- ...device and method for co-locating models on an accelerator based on their computational characteristics and affinity. ...tronic device has processors that analyze computational characteristics of models being located to an accelerator.3 KB (392 words) - 05:25, 2 January 2024
- =TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS= ==TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS - A simplified explanation of the abstract==3 KB (454 words) - 04:30, 11 July 2024
- =DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS= ==DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS - A simplified explanation of the abstract==3 KB (472 words) - 09:21, 12 April 2024
- ...federated learning in computational models. The apparatus determines if a computational model can be trained locally on a client device within a target time. If no * The apparatus determines if a computational model can be trained locally on a client device within a target time.4 KB (483 words) - 00:15, 14 October 2024
- The patent application relates to federated learning for computational models, where an apparatus determines if a model can be trained locally on a clien * Apparatus determines if a computational model can be trained locally within a target time.3 KB (470 words) - 02:16, 18 October 2024
- =METHOD AND SYSTEM FOR PERSONALISING MACHINE LEARNING MODELS= ==METHOD AND SYSTEM FOR PERSONALISING MACHINE LEARNING MODELS - A simplified explanation of the abstract==3 KB (390 words) - 06:15, 1 January 2024
- =DEVICE AND METHOD WITH COMPUTATIONAL MEMORY= ==DEVICE AND METHOD WITH COMPUTATIONAL MEMORY - A simplified explanation of the abstract==4 KB (525 words) - 04:19, 11 April 2024
- =SYSTEM AND METHOD FOR MANAGING AI MODELS USING ANOMALY DETECTION= ==SYSTEM AND METHOD FOR MANAGING AI MODELS USING ANOMALY DETECTION - A simplified explanation of the abstract==4 KB (552 words) - 17:28, 7 July 2024
- =DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS= ==DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS - A simplified explanation of the abstract==4 KB (495 words) - 08:37, 11 April 2024
- =DEVICE AND METHOD WITH COMPUTATIONAL MEMORY= ==DEVICE AND METHOD WITH COMPUTATIONAL MEMORY - A simplified explanation of the abstract==4 KB (529 words) - 07:45, 12 April 2024
- ...TING CLINICAL EFFICACY OF MULTI-LABEL MULTI-CLASS COMPUTATIONAL DIAGNOSTIC MODELS= ...TING CLINICAL EFFICACY OF MULTI-LABEL MULTI-CLASS COMPUTATIONAL DIAGNOSTIC MODELS - A simplified explanation of the abstract==5 KB (701 words) - 10:20, 25 March 2024
- ...ED TRAINING OF GRAPH NEURAL NETWORKS (GNN) BASED KNOWLEDGE GRAPH EMBEDDING MODELS= ...ED TRAINING OF GRAPH NEURAL NETWORKS (GNN) BASED KNOWLEDGE GRAPH EMBEDDING MODELS - A simplified explanation of the abstract==5 KB (627 words) - 03:14, 18 October 2024
- =Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency= ==Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency - A simplified explanation of the abstract==3 KB (468 words) - 17:46, 1 January 2024
- ...ting clinical efficacy of multi-label multi-class computational diagnostic models, hyperspectral artificial vision for machines, and vicarious calibration of ...sultancy Services Limited cover evaluating clinical efficacy of diagnostic models, hyperspectral artificial vision for machines, and vicarious calibration of3 KB (372 words) - 08:30, 27 March 2024
- =GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION= ==GENERATIVE MACHINE LEARNING MODELS FOR PRIVACY PRESERVING SYNTHETIC DATA GENERATION USING DIFFUSION - A simpli4 KB (482 words) - 07:31, 10 April 2024
- =SYSTEM AND METHOD FOR MANAGING AI MODELS USING DIRECT MODIFICATION DETECTION= ==SYSTEM AND METHOD FOR MANAGING AI MODELS USING DIRECT MODIFICATION DETECTION - A simplified explanation of the abstr3 KB (520 words) - 17:28, 7 July 2024
- =Parameter Efficient Prompt Tuning for Efficient Models at Scale= ==Parameter Efficient Prompt Tuning for Efficient Models at Scale - A simplified explanation of the abstract==2 KB (250 words) - 02:12, 19 October 2023
- ...learning neural networks like transformer-based models and large language models. The first pass includes scalar operations to calculate a logarithm and an * Reduces computational cost without loss of accuracy5 KB (664 words) - 03:06, 1 October 2024