CanViT is the first task- and policy-agnostic AVFM pretrained via passive-to-active dense latent distillation on 13.2M scenes and 1B random glimpses, achieving 38.5% ADE20K mIoU in one glimpse and 84.5% ImageNet-1k top-1 after fine-tuning.
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
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In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.
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- abstract In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.
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CanViT: Toward Active-Vision Foundation Models
CanViT is the first task- and policy-agnostic AVFM pretrained via passive-to-active dense latent distillation on 13.2M scenes and 1B random glimpses, achieving 38.5% ADE20K mIoU in one glimpse and 84.5% ImageNet-1k top-1 after fine-tuning.
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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Identifying Latent Concepts and Structures for Generalized Category Discovery
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LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning
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CoMetaPNS combines meta-learned neural surrogates with a continual Bayesian Gaussian Mixture Model to adapt cardiac electrophysiology simulations to new data while avoiding catastrophic forgetting.
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AdaState: Self-Evolving Anchors for Streaming Video Generation
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LC-Flow: Learning Local Continuous Optical Flow and Confidence from events
LC-Flow introduces a continuous local recurrent network for learning sparse optical flow and confidence directly from event streams, with confidence-guided aggregation reaching new SOTA on MVSEC.
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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
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Identify Then Project: Contrastive Learning of Latent Dynamics from Partial Observations with Port-Hamiltonian Structure
A two-stage contrastive teacher-student framework learns and then projects latent dynamics onto port-Hamiltonian submanifolds from partial observations.
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TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment
TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
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Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction
PRISM-VQ integrates vector-quantized latent factors with financial priors and a structure-conditioned mixture-of-experts to deliver improved cross-sectional stock return predictions and portfolio performance on CSI 300 and S&P 500.
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What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization
The What-Where Transformer achieves explicit what-where separation in a ViT-style backbone via concurrent token and attention-map streams, yielding emergent object discovery from attention maps and better weakly-supervised localization.
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TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
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CLOVER augments value decomposition with a GNN mixer whose weights depend on the realized wireless communication graph, proving permutation invariance, monotonicity, and greater expressiveness than QMIX while showing gains on Predator-Prey and Lumberjacks under p-CSMA channels.
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ExDoS: Expert-Guided Dual-Focus Cross-Modal Distillation for Smart Contract Vulnerability Detection
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ComHymba: Low-Complexity Domain-Informed Foundation Model for Wireless Communications
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M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
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Uniform Inductive Spatio-Temporal Kriging
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Short window attention enables long-term memorization
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MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
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