Subset (4)

current · 3.62k rows

Split (1)

train · 3.62k rows

all_domains list	all_methods list	all_novelty_signals list	all_query_ids list	archived bool	candidate_status string	candidate_rule_version string	candidate_eligible bool	candidate_reason string	candidate_evidence list	created_at string	description string	domains list	evidence_signals list	evidence_tier string	evidence_version string	first_observed_at string	fork bool	forks int64	github_id int64	homepage string	language string	license string	methods list	name string	novelty_signals list	observation_count int64	observed_at string	paper_ids list	pushed_at string	query_ids list	readme_blob_sha string	readme_checked_at string	readme_evidence_version string	readme_etag string	readme_sections list	readme_signals list	readme_status string	readme_observed_at string	readme_repository_name_at_fetch string	selection_reason string	selection_signals list	selection_status string	selection_version string	stars int64	topics list	updated_at string	url string	extra_json string
[ "information-retrieval", "natural-language-processing" ]	[ "cross-encoder", "neural-reranking", "rag" ]	[ "description", "query-match", "repository-metadata" ]	[ "nlp.cross-encoder-reranking" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-18T14:42:59Z	A new architecture of RAG	[ "information-retrieval", "natural-language-processing" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-26T16:16:45Z	false	0	1,424,476,053	null	Python	null	[ "cross-encoder", "neural-reranking", "rag" ]	Sanidhya555/Documind	[ "description", "query-match", "repository-metadata" ]	1	2026-10-26T16:16:45Z	[]	2026-10-18T14:57:21Z	[ "nlp.cross-encoder-reranking" ]	null	null	null	null	null	null	null	null	null	specific-method-with-novelty-claim	[ "method-tied-novelty-claim", "ml-method-cue" ]	include	ml-contribution-v5	0	[]	2026-10-18T14:57:25Z	https://github.com/Sanidhya555/Documind	null
[ "deep-learning", "recommender-systems", "representation-learning" ]	[ "contrastive-learning", "self-supervised-learning" ]	[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]	[ "general.contrastive-learning" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-20T00:48:44Z	[SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".	[ "deep-learning", "recommender-systems", "representation-learning" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-20T14:21:44Z	false	0	1,426,338,283	https://arxiv.org/abs/2304.07763v1	null	MIT	[ "contrastive-learning", "self-supervised-learning" ]	AliasJeff/MCLRec	[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]	2	2026-10-23T14:17:19Z	[]	2026-10-20T05:31:23Z	[ "general.contrastive-learning" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[]	2026-10-20T01:04:47Z	https://github.com/AliasJeff/MCLRec	null
[ "computer-vision", "generative-ai", "generative-modeling", "multimodal", "natural-language-processing", "vision" ]	[ "diffusion", "generative-modeling", "image-generation", "text-to-image" ]	[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]	[ "multimodal.text-to-image", "vision.image-diffusion" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-20T01:37:19Z	Official implementation of CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation (arXiv:2610.29415).	[ "computer-vision", "generative-ai", "generative-modeling", "natural-language-processing" ]	[ "diffusion-model" ]	ml_related_text	gh-ml-relevance-v1	2026-10-21T14:32:05Z	false	0	1,426,371,704	https://arxiv.org/abs/2610.29415	Python	MIT	[ "diffusion", "generative-modeling" ]	ryosuke-yamaki/ccg-diffusion	[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]	3	2026-10-24T13:23:18Z	[]	2026-10-20T10:02:45Z	[ "vision.image-diffusion" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[ "ccg", "nlp", "pytorch", "sana", "stable-diffusion", "syntactic-parsing", "text-to-image-generation" ]	2026-10-22T00:56:52Z	https://github.com/ryosuke-yamaki/ccg-diffusion	null
[ "distributed-ml", "privacy-and-federated-learning" ]	[ "collaborative-learning", "federated-learning" ]	[ "description", "query-match", "repository-metadata" ]	[ "trust.federated-learning" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-20T06:57:21Z	This is the official code repository of the paper "A Byzantine-Robust Federated Learning with Nesterov Momentum".	[ "distributed-ml", "privacy-and-federated-learning" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-21T14:32:05Z	false	0	1,426,625,722	null	Python	null	[ "collaborative-learning", "federated-learning" ]	IHXU/Byrd-NAFL-IOTJ	[ "description", "query-match", "repository-metadata" ]	2	2026-10-24T13:23:18Z	[]	2026-10-20T07:11:23Z	[ "trust.federated-learning" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[]	2026-10-20T07:19:25Z	https://github.com/IHXU/Byrd-NAFL-IOTJ	null
[ "computer-vision", "embodied-ai", "generative-modeling", "language", "multimodal", "multimodal-learning", "robotics", "vision" ]	[ "contrastive-learning", "diffusion", "flow-matching", "robot-learning", "vision-language-action", "vision-language-model" ]	[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]	[ "multimodal.vision-language", "robotics.visual-language-action" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-21T04:50:43Z	Official code for "Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models" — self-contained openpi-based implementation, paper run configs, diagnostics, and LIBERO evaluation.	[ "computer-vision", "embodied-ai", "generative-modeling", "language", "multimodal", "multimodal-learning", "robotics", "vision" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-23T14:17:19Z	false	0	1,427,890,827	https://arxiv.org/abs/2606.05737	Python	Apache-2.0	[ "contrastive-learning", "diffusion", "flow-matching", "robot-learning", "vision-language-action", "vision-language-model" ]	bondtesty/let-it-be-simple	[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]	1	2026-10-23T14:17:19Z	[]	2026-10-22T11:04:35Z	[ "multimodal.vision-language", "robotics.visual-language-action" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	2	[ "diffusion-policy", "flow-matching", "imitation-learning", "libero", "one-step-generation", "robot-learning", "vision-language-action" ]	2026-10-23T13:55:04Z	https://github.com/bondtesty/let-it-be-simple	null
[ "efficient-ml", "model-compression" ]	[ "distillation", "knowledge-distillation" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "efficiency.knowledge-distillation" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-22T02:53:56Z	The official implementation of KD paper in IJCV: "Relaxed Knowledge Distillation"	[ "efficient-ml", "model-compression" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-23T14:17:19Z	false	0	1,429,168,600	null	Python	MIT	[ "distillation", "knowledge-distillation" ]	we1sher/ReKD	[ "description", "license-metadata", "query-match", "repository-metadata" ]	1	2026-10-23T14:17:19Z	[]	2026-10-22T03:28:21Z	[ "efficiency.knowledge-distillation" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[]	2026-10-22T05:57:02Z	https://github.com/we1sher/ReKD	null
[ "embodied-ai", "generative-modeling", "reinforcement-learning", "robotics" ]	[ "batch-reinforcement-learning", "diffusion", "diffusion-policy", "imitation-learning", "offline-reinforcement-learning", "reinforcement-learning" ]	[ "description", "query-match", "repository-metadata" ]	[ "rl.offline", "robotics.diffusion-policy" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-22T10:24:17Z	Official implementation of "Trajectory-Consistent Diffusion Policies for Offline Reinforcement Learning" (NeurIPS 2026).	[ "embodied-ai", "generative-modeling", "reinforcement-learning", "robotics" ]	[ "reinforcement-learning" ]	direct_ml_text	gh-ml-relevance-v1	2026-10-23T14:17:19Z	false	0	1,429,584,024	null	null	null	[ "batch-reinforcement-learning", "diffusion", "diffusion-policy", "imitation-learning", "offline-reinforcement-learning", "reinforcement-learning" ]	yichaofu/TCR-Diffusion	[ "description", "query-match", "repository-metadata" ]	1	2026-10-23T14:17:19Z	[]	2026-10-22T10:24:19Z	[ "rl.offline", "robotics.diffusion-policy" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	0	[]	2026-10-22T10:24:23Z	https://github.com/yichaofu/TCR-Diffusion	null
[ "generative-ai", "generative-modeling", "language", "natural-language-processing" ]	[ "diffusion", "language-model", "pretraining" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "llm.large-language-model" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-23T04:39:58Z	An official implementation of "Global Context Guidance for Diffusion Large Language Models" (NeurIPS 2026) in PyTorch.	[ "generative-ai", "generative-modeling", "language", "natural-language-processing" ]	[ "large-language-model" ]	direct_ml_text	gh-ml-relevance-v1	2026-10-23T14:17:19Z	false	0	1,430,581,146	null	Python	GPL-3.0	[ "diffusion", "language-model", "pretraining" ]	cvlab-yonsei/GCG	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-25T13:45:15Z	[]	2026-10-23T04:44:56Z	[ "llm.large-language-model" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	1	[]	2026-10-23T10:38:10Z	https://github.com/cvlab-yonsei/GCG	null
[ "computational-science", "physics" ]	[ "neural-operator", "neural-operators", "surrogate-modeling" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "science.neural-operators" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-23T05:03:34Z	The official code repository of paper "Time-Lapse Seismic Inversion Using a 3D Neural Operator " .	[ "computational-science", "physics" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-23T14:17:19Z	false	0	1,430,597,582	null	Jupyter Notebook	MIT	[ "neural-operator", "neural-operators", "surrogate-modeling" ]	maxiao6668/Time_Lapse_inversion	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-25T13:45:15Z	[]	2026-10-23T05:10:20Z	[ "science.neural-operators" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[]	2026-10-23T05:10:23Z	https://github.com/maxiao6668/Time_Lapse_inversion	null
[ "computer-vision", "information-retrieval" ]	[ "distillation", "image-retrieval", "metric-learning" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "vision.image-retrieval" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-23T17:52:40Z	Official PyTorch implementation of The Locality Trade-off in Masked Patch Self-Distillation (NeurIPS 2026)	[ "computer-vision", "information-retrieval" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-24T13:23:18Z	false	0	1,431,374,793	null	Python	Apache-2.0	[ "distillation", "image-retrieval", "metric-learning" ]	winci-ai/jea	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-26T16:11:12Z	[]	2026-10-25T12:06:11Z	[ "vision.image-retrieval" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	1	[]	2026-10-25T12:06:15Z	https://github.com/winci-ai/jea	null
[ "computer-vision", "efficient-ml", "general-ml", "generative-ai", "language", "machine-learning-systems", "model-compression" ]	[ "language-model", "post-training-quantization", "quantization", "transformer" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "efficiency.post-training-quantization", "efficiency.quantization", "llm.transformers", "recall.name.transformer-model" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-24T11:30:20Z	Official PyTorch implementation of "GPFA-Q: Post-Training Quantization with Gradient-Projected Fisher Approximation for Vision Transformers", [NeurIPS 2026.]	[ "computer-vision", "efficient-ml", "general-ml", "generative-ai", "language", "machine-learning-systems", "model-compression" ]	[ "transformer" ]	ml_related_text	gh-ml-relevance-v1	2026-10-25T13:45:15Z	false	0	1,432,113,861	null	Python	Apache-2.0	[ "language-model", "post-training-quantization", "quantization", "transformer" ]	Nobody-96/GPFA-Q	[ "description", "license-metadata", "query-match", "repository-metadata" ]	1	2026-10-25T13:45:15Z	[]	2026-10-25T11:10:16Z	[ "efficiency.post-training-quantization", "efficiency.quantization", "llm.transformers", "recall.name.transformer-model" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	7	[]	2026-10-25T12:37:17Z	https://github.com/Nobody-96/GPFA-Q	null
[ "generative-ai", "generative-modeling", "language" ]	[ "diffusion", "fine-tuning", "parameter-efficient-fine-tuning" ]	[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]	[ "llm.finetuning" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-24T15:30:53Z	Official PyTorch implementation of "Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation" (NeurIPS 2026).	[ "generative-ai", "generative-modeling", "language" ]	[ "diffusion-model" ]	ml_related_text	gh-ml-relevance-v1	2026-10-25T13:45:15Z	false	0	1,432,322,267	https://xuanqi99.github.io/MUSE/	Python	MIT	[ "diffusion", "fine-tuning", "parameter-efficient-fine-tuning" ]	xuanqi99/MUSE-Code	[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]	1	2026-10-25T13:45:15Z	[]	2026-10-24T16:04:22Z	[ "llm.finetuning" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	0	[ "diffusion-models", "generative-modeling", "parameter-efficient-fine-tuning", "unsupervised-domain-adaptation" ]	2026-10-24T19:25:15Z	https://github.com/xuanqi99/MUSE-Code	null
[ "computational-science", "physics" ]	[ "neural-operators", "surrogate-modeling" ]	[ "description", "query-match", "repository-metadata" ]	[ "science.neural-operators" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-25T05:05:59Z	Official implementation of "CoupledFlow: One-Step Neural Operators for Coupled Multi-Physics PDEs", which is accpeted at NeurIPS 2026	[ "computational-science", "physics" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-25T13:45:15Z	false	0	1,432,848,066	null	null	null	[ "neural-operators", "surrogate-modeling" ]	khiembk/CoupledFlow	[ "description", "query-match", "repository-metadata" ]	2	2026-10-27T15:25:06Z	[]	2026-10-25T05:06:46Z	[ "science.neural-operators" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	0	[]	2026-10-25T05:06:51Z	https://github.com/khiembk/CoupledFlow	null
[ "sequence-modeling", "time-series" ]	[ "sequence-modeling", "state-space-model" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "timeseries.state-space-models" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-25T09:19:18Z	Official PyTorch implementation of the ECCV 2026 paper "S2Gest: Split-Scan State Space Models for Dynamic Hand Gesture Recognition".	[ "sequence-modeling", "time-series" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-26T16:11:12Z	false	0	1,433,020,480	null	Python	MIT	[ "sequence-modeling", "state-space-model" ]	Chen-Ke-Fan/S2Gest	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-28T16:08:23Z	[]	2026-10-25T10:27:39Z	[ "timeseries.state-space-models" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[]	2026-10-25T15:23:33Z	https://github.com/Chen-Ke-Fan/S2Gest	null
[ "computer-vision", "generative-ai" ]	[ "generative-modeling", "video-generation" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "vision.video-generation" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-25T10:20:44Z	[NeurIPS 2026] Official implementation of FreeSpec: Training-Free Long Video Generation via Singular-Spectrum Reconstruction	[ "computer-vision", "generative-ai" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-26T16:11:12Z	false	0	1,433,069,030	https://fdchen24.github.io/FreeSpec-Website/	null	Apache-2.0	[ "generative-modeling", "video-generation" ]	fdchen24/FreeSpec	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-28T16:08:23Z	[]	2026-10-26T04:39:22Z	[ "vision.video-generation" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	0	[]	2026-10-26T04:39:26Z	https://github.com/fdchen24/FreeSpec	null
[ "computer-vision", "language", "multimodal", "multimodal-learning", "natural-language-processing", "vision" ]	[ "contrastive-learning", "image-text", "multimodal-learning", "vision-language", "vision-language-model" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "multimodal.vision-language", "multimodal.vlm", "vision.vision-language" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-26T03:15:55Z	[NeurIPS 2026] Official implementation of paper "SwiftVLM: Efficient Vision-Language Model Inference via Cross-Layer Token Bypass"	[ "computer-vision", "multimodal", "multimodal-learning", "natural-language-processing" ]	[ "model-inference" ]	ml_related_text	gh-ml-relevance-v1	2026-10-27T15:25:06Z	false	0	1,433,849,114	null	null	MIT	[ "multimodal-learning", "vision-language" ]	qianchen214/SwiftVLM	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-28T16:08:23Z	[]	2026-10-26T03:15:57Z	[ "vision.vision-language" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	2	[]	2026-10-28T07:19:18Z	https://github.com/qianchen214/SwiftVLM	null
[ "classical-ml", "deep-learning", "forecasting", "representation-learning", "time-series", "time-series-and-forecasting" ]	[ "contrastive-learning", "forecasting", "metric-learning", "representation-learning", "self-supervised-learning", "sequence-modeling" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "general.contrastive-learning", "general.metric-learning", "timeseries.forecasting" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-26T14:06:26Z	In this code repo we present a new representation learning method for time series forecasting, specifically applied to electricity load forecasting. This new representation learning method is called similarity preserving, based on similarity metric learning like contrastive learning but much more efficient and with oth...	[ "forecasting", "time-series", "time-series-and-forecasting" ]	[ "representation-learning" ]	direct_ml_text	gh-ml-relevance-v1	2026-10-27T15:25:06Z	false	0	1,434,468,308	null	Python	MIT	[ "contrastive-learning", "forecasting", "sequence-modeling" ]	lear-ner97/Similarity-preserving-representation-learning-for-weekly-electricity-load-forecasting	[ "description", "license-metadata", "query-match", "repository-metadata" ]	2	2026-10-28T16:08:23Z	[]	2026-10-26T14:11:48Z	[ "timeseries.forecasting" ]	null	null	null	null	null	null	null	null	null	specific-method-with-novelty-claim	[ "method-tied-novelty-claim", "ml-method-cue" ]	include	ml-contribution-v5	0	[]	2026-10-26T17:46:32Z	https://github.com/lear-ner97/Similarity-preserving-representation-learning-for-weekly-electricity-load-forecasting	null
[ "generative-ai", "generative-modeling" ]	[ "diffusion" ]	[ "description", "query-match", "repository-metadata" ]	[ "llm.diffusion-phrase" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-27T08:27:48Z	An official implementation for NeurIPS 2026 paper "AdderDiff: All-Addition Spiking Diffusion Models with Attention Enhancement"	[ "generative-ai", "generative-modeling" ]	[ "diffusion-model" ]	ml_related_text	gh-ml-relevance-v1	2026-10-27T15:25:06Z	false	0	1,435,451,886	null	Python	null	[ "diffusion" ]	hanbaobao950123/AdderDiff	[ "description", "query-match", "repository-metadata" ]	2	2026-10-29T16:14:17Z	[]	2026-10-27T09:28:26Z	[ "llm.diffusion-phrase" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	0	[]	2026-10-27T12:00:12Z	https://github.com/hanbaobao950123/AdderDiff	null
[ "efficient-ml", "general-ml", "model-compression" ]	[ "deep-learning", "distillation", "knowledge-distillation" ]	[ "description", "github-topics", "query-match", "repository-metadata" ]	[ "efficiency.knowledge-distillation", "general.topic-deep-learning" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-28T00:56:54Z	A novel knowledge distillation technique beyond Teacher-Student. FFTD trains a new model using a peer (Friend) and an expert (Teacher's Friend), enforcing independence via a negative similarity penalty. Proof of Concept on CPU.	[ "efficient-ml", "general-ml", "model-compression" ]	[ "deep-learning", "transformer" ]	direct_ml_text	gh-ml-relevance-v1	2026-10-29T16:14:17Z	false	0	1,436,445,742	null	Python	null	[ "deep-learning", "distillation", "knowledge-distillation" ]	mouad-tarif/fftd-distillation	[ "description", "github-topics", "query-match", "repository-metadata" ]	1	2026-10-29T16:14:17Z	[]	2026-10-28T03:12:03Z	[ "efficiency.knowledge-distillation", "general.topic-deep-learning" ]	null	null	null	null	null	null	null	null	null	specific-method-with-novelty-claim	[ "contribution-language", "method-tied-novelty-claim", "ml-method-cue" ]	include	ml-contribution-v5	0	[ "ai-research", "deep-learning", "fftd", "knowledge-distillation", "transformers" ]	2026-10-28T03:12:07Z	https://github.com/mouad-tarif/fftd-distillation	null
[ "deep-learning", "efficient-ml", "generative-ai", "generative-modeling", "language", "natural-language-processing" ]	[ "conditional-computation", "diffusion", "mixture-of-experts", "sparse-activation" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "efficiency.mixture-of-experts", "recall.name.diffusion-model" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-29T09:43:40Z	Official PyTorch implementation of the paper "PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts"	[ "deep-learning", "efficient-ml", "generative-ai", "generative-modeling", "language", "natural-language-processing" ]	[ "large-language-model" ]	direct_ml_text	gh-ml-relevance-v1	2026-10-29T16:14:17Z	false	1	1,438,219,171	null	Python	MIT	[ "conditional-computation", "diffusion", "mixture-of-experts", "sparse-activation" ]	KlingAIResearch/PixelMoE	[ "description", "license-metadata", "query-match", "repository-metadata" ]	1	2026-10-29T16:14:17Z	[]	2026-10-29T09:57:28Z	[ "efficiency.mixture-of-experts", "recall.name.diffusion-model" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]	include	ml-contribution-v5	5	[]	2026-10-29T13:03:46Z	https://github.com/KlingAIResearch/PixelMoE	null
[ "3d", "computer-vision", "robotics" ]	[ "3d-object-detection", "3d-perception", "mamba" ]	[ "description", "license-metadata", "query-match", "repository-metadata" ]	[ "multimodal.3d-object-detection" ]	false	candidate	ml-candidate-v4	true	selected-by-current-rule	[]	2026-10-29T15:47:24Z	Official implementation of PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection (ECCV 2026)	[ "3d", "computer-vision", "robotics" ]	[]	no_text_signal	gh-ml-relevance-v1	2026-10-29T16:14:17Z	false	0	1,438,630,366	https://pointlam.github.io/	Python	Apache-2.0	[ "3d-object-detection", "3d-perception", "mamba" ]	VISION-SJTU/PointLAM	[ "description", "license-metadata", "query-match", "repository-metadata" ]	1	2026-10-29T16:14:17Z	[]	2026-10-29T15:47:38Z	[ "multimodal.3d-object-detection" ]	null	null	null	null	null	null	null	null	null	official-paper-method-implementation	[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]	include	ml-contribution-v5	0	[]	2026-10-29T16:26:18Z	https://github.com/VISION-SJTU/PointLAM	null

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.