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<marker id="arrow-gre... | This diagram illustrates a system architecture for Mixture-of-Experts (MoE) models, divided into a "Profiling Phase" and an "Online Phase." The top-left section shows the "MoE Layer X" where "input tokens" pass through a "Gating Network" to select specific experts, leading to an "Expert Affinity Matrix" that visualizes... | vfig_diagrams |
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<rect x="2" y="2" width="596" height="316" rx="3... | This diagram illustrates a machine learning architecture featuring a "High Dimensional Data" input block on the left and a "Low Dim. Rep." output block on the right. In the center, two distinct processing blocks are positioned vertically: a larger, light-blue "Teacher Network (Representation Learning Network or Manifol... | vfig_diagrams |
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font-family:... | This diagram illustrates the Scaled Dot-Product Attention mechanism, starting with inputs Q, K, and V at the bottom. The Q and K inputs feed into a "MatMul" block, followed by "Scale," "Mask (opt.)," and "SoftMax" layers. The output of the SoftMax layer is combined with the V input through a final "MatMul" operation at... | vfig_diagrams |
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... | This diagram illustrates a multi-stage problem-solving workflow, beginning with an "Original Problem" box containing a multiple-choice math question about the prime factors of 2010. A horizontal arrow leads to a "Key Information Extraction" block, which details specific analytical steps like "core_mathematical_concept,... | vfig_diagrams |
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<rect width="100%" hei... | This flow diagram illustrates a technical process starting with an "Input Video Sequence" box on the left. The input branches into two parallel paths leading to "A. Point Tracking" and "C. Supervoxel Clustering." From "A. Point Tracking," the flow continues to "B. Motion Clustering." Both "B. Motion Clustering" and "C.... | vfig_diagrams |
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<marker id="arrow-orange"... | This diagram illustrates an iterative process of refining a Python web crawler through two rounds of development. Each round displays an "Intent Tree" on the left, a code snippet in the center, and a corresponding "Task Graph" on the right, showing how user feedback updates the system's logic. In "Round t+1," the inten... | vfig_diagrams |
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.title { font-size: 20px; fon... | This diagram uses a Venn-like structure to categorize four distinct roles within a community: New Comers, Mentors, Moderators, and Experts. A light blue circle at the top left labeled "New Comers" describes them as "Problem focused" and "Curious," with accompanying text noting they initiate contact to get questions ans... | vfig_diagrams |
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<marker id="arrow-r... | This diagram illustrates the "Training & Inference: F-GMRES" process through a series of interconnected neural network layers and mathematical operations. At the top, a "Residual r_k" block feeds into a "Fourier layer," which branches into a parallel path containing a weight "W" and a sequence of operations involving "... | vfig_diagrams |
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... | This technical flowchart illustrates a machine learning process divided into three distinct color-coded stages. The top section, labeled "Data collection and preprocessing," begins with "Data collection" flowing into "Data preprocessing using feature selection algorithms," which then splits into "Existing sensor datase... | vfig_diagrams |
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<st... | This technical diagram illustrates a data processing flow using a series of connected nodes arranged in a hierarchical structure. At the top level, two light-blue rectangular nodes labeled "Hallucination detector" and "Fact checker" serve as the starting points. Both of these nodes have arrows pointing downward to a ce... | vfig_diagrams |
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... | This diagram illustrates the architecture of a Feature Pyramid Network (FPN) built upon a ResNet-50 backbone. The ResNet-50 section extracts feature maps labeled C2 through C5, which are then processed through lateral 1x1 convolutions. These features are combined with up-sampled maps from higher levels using element-wi... | vfig_diagrams |
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<marker id="arrow-pu... | This technical diagram illustrates an architecture involving an "Actor" and a "Critic," both utilizing a "Graph Neural Network" to process input states. On the left, a pink box contains four agents labeled $s^1$, $s^2$, $s^3$, and $s^4$, along with a central element $s^r$, which connect via colored lines to the Actor a... | vfig_diagrams |
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... | This image illustrates a token caching mechanism where sequences are divided into "Initial Tokens" (yellow), "Evicted Tokens" (grey), and "Recent tokens" (blue/red). As tokens 7, 8, and 9 are generated, the sequence shifts, causing middle tokens to be evicted while maintaining the initial and recent segments. The right... | vfig_diagrams |
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.math-text { font-famil... | This diagram illustrates two parallel processes, labeled A and B, which involve aligning data representations. Each process features two horizontal bars composed of five alternating colored segments. The top bars are enclosed in dashed-line rectangles and labeled $h^A \leftarrow T^A(s_k^A)$ and $h^B \leftarrow T^B(s_k^... | vfig_diagrams |
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... | This technical diagram illustrates a two-stage workflow for RTL testing, with a separate section for generating buggy code. At the top, "RTL Description" and "RTL Code (Unknown quality)" feed into "Stage 1: Test Plan Generation," which uses an "Expert Test Plan LLM" to produce a "Test Plan." This output, along with the... | vfig_diagrams |
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