#highdimensional — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #highdimensional, aggregated by home.social.
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Origin 006 Core đạt bước tiến lớn: xử lý 100.000 điểm dữ liệu trong 14.73s tại 200D – không dùng GPU, không backprop. Tốc độ 6,788 điểm/giây, độ trễ trung bình 147μs, nén dữ liệu 50.04% bằng hình học định hướng xác định. Chạy trên CPU Colab thông thường. Purity Mode giúp duy trì cấu trúc trong không gian cao chiều. Mở ra hướng mới cho xử lý dữ liệu hiệu năng cao, tiết kiệm năng lượng. #AI #MachineLearning #Origin006 #DeterministicAI #HighDimensional #LLM #TríTuệNhânTạo #HọcMáy #XửLýDữLiệu
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I like that someone did a followup to 3blue1brown video on near-orthogonality:
Beyond Orthogonality: How Language Models Pack Billions of Concepts into 12,000 Dimensions
https://nickyoder.com/johnson-lindenstrauss/
> This research suggests that current embedding dimensions (1,000-20,000) provide more than adequate capacity for representing human knowledge and reasoning. The challenge lies not in the capacity of these spaces but in learning the optimal arrangement of concepts within them.
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🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌
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🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌
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🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌
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🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌
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🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌
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Looks like advances in neural computing theory might have analog computing back with a vengueance. 🤯
#reservoircomputing #analog #computing #highdimensional #computingspace
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Looks like advances in neural computing theory might have analog computing back with a vengueance. 🤯
#reservoircomputing #analog #computing #highdimensional #computingspace
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Looks like advances in neural computing theory might have analog computing back with a vengueance. 🤯
#reservoircomputing #analog #computing #highdimensional #computingspace
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Looks like advances in neural computing theory might have analog computing back with a vengueance. 🤯
#reservoircomputing #analog #computing #highdimensional #computingspace
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Looks like advances in neural computing theory might have analog computing back with a vengueance. 🤯
#reservoircomputing #analog #computing #highdimensional #computingspace
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Does anyone know of a research project which explored structuring oppositions in #highdimensional spaces constructed from fictional narratives? #digitalhumanities
I have come across this only from an approach in art history, where the artworks represented in the vector space were distributed in clusters, and therefore represented oppositions such as naturalism vs. impressionism.
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Can someone with better intuition about #highDimensional space tell me if this is right?