CHASM introduces a cross-frequency harmonized axis-separable spectral mixer using a shared channel eigenbasis plus per-frequency positive gains, yielding consistent gains over same-backbone baselines in medical and natural image tasks.
hub
Simba: Simplified mamba-based architecture for vision and multivariate time series
14 Pith papers cite this work. Polarity classification is still indexing.
14
Pith papers citing it
hub tools
citation-role summary
background 1
baseline 1
citation-polarity summary
representative citing papers
A real Schur decomposition projection maps the state matrix of discrete-time state-space layers onto its nearest stable counterpart, delivering accuracy comparable to prior stable identification methods with fewer weights.
GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Transformer MLLMs.
Titans combine attention for current context with a learnable neural memory for long-term history, achieving better performance and scaling to over 2M-token contexts on language, reasoning, genomics, and time-series tasks.
TopoMamSurv introduces topology-aware ordering and bidirectional Mamba with GCN for efficient WSI graph survival analysis, claiming performance gains on five TCGA datasets.
A dynamics-informed Temporal Fusion Transformer surrogate emulates stochastic tipping events in global ocean transport simulations with 465x speedup and high-fidelity timing predictions.
Mamba-3 architectural changes made for hyperscale GPUs raise edge latency 28% at 880M parameters and 48% at 15M parameters relative to earlier Mamba designs.
DMbaGCN combines a local state-evolution Mamba for node-specific dynamics with a global context-aware Mamba to reduce over-smoothing in deep graph neural networks.
Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.
citing papers explorer
-
CHASM: Cross-frequency Harmonized Axis-Separable Mixing for Spectral Token Operators
CHASM introduces a cross-frequency harmonized axis-separable spectral mixer using a shared channel eigenbasis plus per-frequency positive gains, yielding consistent gains over same-backbone baselines in medical and natural image tasks.
-
A Novel Schur-Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures
A real Schur decomposition projection maps the state matrix of discrete-time state-space layers onto its nearest stable counterpart, delivering accuracy comparable to prior stable identification methods with fewer weights.
-
GeoCert: Certified Geometric AI for Reliable Forecasting
GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
-
NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
-
HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
-
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Transformer MLLMs.
-
Titans: Learning to Memorize at Test Time
Titans combine attention for current context with a learnable neural memory for long-term history, achieving better performance and scaling to over 2M-token contexts on language, reasoning, genomics, and time-series tasks.
-
Graph Mamba Survival Analysis Based on Topology-Aware ordering
TopoMamSurv introduces topology-aware ordering and bidirectional Mamba with GCN for efficient WSI graph survival analysis, claiming performance gains on five TCGA datasets.
-
Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics
A dynamics-informed Temporal Fusion Transformer surrogate emulates stochastic tipping events in global ocean transport simulations with 465x speedup and high-fidelity timing predictions.
-
The Hyperscale Lottery: How State-Space Models Have Sacrificed Edge Efficiency
Mamba-3 architectural changes made for hyperscale GPUs raise edge latency 28% at 880M parameters and 48% at 15M parameters relative to earlier Mamba designs.
-
Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling
DMbaGCN combines a local state-evolution Mamba for node-specific dynamics with a global context-aware Mamba to reduce over-smoothing in deep graph neural networks.
-
Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation
Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.
-
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.
- Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba