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This paper presents V2TATC, a joint voice–trajectory embedding framework for air traffic controller situational awareness, and introduces a novel dataset for cross-modal retrieval experiments in congested airspaces.
This paper proposes a unified path-integral framework for generative modeling, showing that flow-based, diffusion-based, variational, and adversarial models arise from different evaluation principles of a single master action. It derives a one-loop correction that reduces tree-level error dramatically and introduces a response-weighted score-matching objective.
This paper unifies twelve continuous-time generative models under mean-field game theory via a cost tuple, introduces MFGLab (a PyTorch library that auto-shares training loops and solvers), and proposes DI-Flow with differentiable entropy for better mode coverage.
Proposes Hybrid Latent-Structural Fusion (HLSF), a weighted anomaly fusion framework combining CP-APR structural anomaly scores with latent-space density scores from normalizing flows, improving cyber anomaly detection on real-world compromised user credentials data.
This paper proposes NFTR, a method for offline goal-conditioned reinforcement learning that uses normalizing flows for subgoal policies and a triangle-slack reweighting to address optimistic bias and mode collapse in hierarchical implicit Q-learning.
Warp RL replaces additive residual corrections in reinforcement learning with an invertible, state-conditioned transformation of the base policy's action distribution using monotonic rational-quadratic spline flows, enabling adaptation of distribution shape, scale, and geometry under dynamics shifts. It matches or outperforms residual correction in ManiSkill3 manipulation tasks and achieves 30% faster task completion in a real robot peg-insertion task.
This paper presents a physics-informed conditional normalizing flow model for angles-only orbit determination in the cislunar environment, enabling flexible posterior representation and providing warm starts for classical algorithms.
MIMFlow integrates Masked Image Modeling with Normalizing Flows for end-to-end image generation, achieving a FID of 2.50 on ImageNet 256x256 with 50% fewer tokens than standard models.
A deep learning framework for probabilistic CO2 column retrieval from OCO-2 spectra using Laplace approximations and normalizing flows, achieving faster inference and better uncertainty quantification than traditional methods.
Proposes NF-CoT, a latent reasoning framework using normalizing flows to model continuous thoughts in LLMs, preserving autoregressive advantages and achieving better code generation performance with lower cost.
Proposes a 'lift' method for training input-convex neural networks (ICNNs) that uses an unconstrained hypernetwork to emit non-negative inter-layer weights, softening the loss landscape and escaping gradient attenuation, achieving lower test loss than projected gradient descent and softplus reparametrization.
This paper introduces Normalizing Trajectory Models (NTM), a novel approach to diffusion-based generation that models reverse steps as conditional normalizing flows with exact likelihood training. NTM enables high-quality text-to-image generation in just four steps while retaining the likelihood framework, outperforming baselines on standard benchmarks.
STARFlow2 is a new research paper introducing an architecture that bridges language models and autoregressive normalizing flows for unified multimodal generation. It addresses structural mismatches in existing systems by using a shared causal masking mechanism for interleaved text-image sequences.
FFJORD introduces a scalable reversible generative model using continuous dynamics and Hutchinson's trace estimator to enable unbiased log-density estimation without architectural constraints. The method achieves state-of-the-art results on density estimation and image generation while maintaining efficient sampling.