Continuous Normalizing Flow
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Related Articles from SNS
Local Diagnostics of Continuous Normalizing Flow for Out-of-Distribution Detection
Announce Type: cross Abstract: We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using continuous normalizing flows (CNFs), we propose a Lagrangian sub-flow (LSF) framework designed to isolate and estimate the density for the relevant components in the representation and using the remaining components as context. Through experimentation with models for speech synthesis, we show that CNFs, similarly...
Latent Reasoning with Normalizing Flows
arXiv:2606.06447v1 Announce Type: new Abstract: Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces this computation through a discrete, serial, and communication-oriented token stream: each reasoning step must be verbalized before the model can proceed, even when the underlying update is semantic, uncertain, or only partially formed. Latent reasoning offers a...
Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
Announce Type: replace Abstract: Continuous Normalizing Flows (CNFs) enable elegant generative modeling but remain bottlenecked by their iterative nature requiring costly sampling and lacking interpretability of the intermediate states. Recent approaches accelerate sampling by straightening trajectories or distilling endpoints, yet they treat the original generative process as a black box, discarding the teacher's intermediate dynamics. We propose a fundamentally different perspective:...
Identifying Connectivity Distributions from Neural Dynamics Using Flows
arXiv:2603.26506v2 Announce Type: replace-cross Abstract: Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical dynamics. Recent work uses low-rank recurrent neural networks (lrRNNs) to infer low-dimensional latent dynamics and connectivity from observed activity, enabling a mechanistic interpretation of the dynamics. However, standard approaches for training lrRNNs can...
A prognostic human brain network for diffuse midline glioma
Abstract Diffuse midline gliomas (DMGs) are near-universally lethal tumours of the childhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3,4,5,6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3,4,5,6,7,8,9 and other glial malignancies10,11,12 through paracrine mechanisms and direct neuron-to-glioma synapses.
FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems
arXiv:2606.03430v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline. Current defences against model extraction rely either on identity-bound query monitoring, which is ineffective against distributed attackers (Sybil), or on prediction poisoning through soft-label perturbation, which is inapplicable to hard-label...
Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
Announce Type: replace-cross Abstract: Inspired by the Equation-Free paradigm, we propose an ``embed-learn-lift'' framework for constructing minimal-dimensional surrogate ROMs for the numerical analysis of high-fidelity Navier-Stokes simulations, even in the presence of symmetries that standard machine-learning surrogates often fail to preserve. The framework consists of four main stages.
Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
Announce Type: replace Abstract: Inspired by the Equation-Free paradigm, we propose an ``embed-learn-lift'' framework for constructing minimal-dimensional surrogate ROMs for the numerical analysis of high-fidelity Navier-Stokes simulations, even in the presence of symmetries that standard machine-learning surrogates often fail to preserve. The framework consists of four main stages.
Experts suspect state policy is putting some farmers in artificial drought
Farmer says flood plain harvesting has forced him to run cattle by the roadside Fri 5 Jun 2026 at 5:30am Paul Cameron watches his mob of thirsty cattle sink their heads into the Goan waterhole on the outskirts of Trangie in western New South Wales. Mr Cameron has driven his cattle to graze the scrubby patches of Crown land hugging the public billabong for more than a year. "I've been walking the stock to town … because we haven't got any water in our own system," Mr Cameron said.
Hacking your PC using your speaker without ever touching it
In my last post, I talked about reverse engineering my new Creative Sound Blaster Katana V2X's firmware. What initially started as simply wanting to write a Linux tool for communicating with my speaker ended up with me discovering vulnerabilities which allow any attacker within a ~15M range of any Katana V2X to turn it into a covert spying tool and Rubber Ducky - all without ever having to pair with or physically touch the device. CTprotocol background As I explained in my previous post, the...