Preview tool helps makers visualize 3D-printed objects
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
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Empirical Likelihood for Nonsmooth Functionals
arXiv:2603.27743v1 Announce Type: cross Abstract: Empirical likelihood is an attractive inferential framework that respects natural parameter boundaries, but existing approaches typically require smoothness of the functional and miscalibrate substantially when these assumptions are violated. For the optimal-value functional central to…
On the Hardness of Reinforcement Learning with Transition Look-Ahead
arXiv:2510.19372v2 Announce Type: replace-cross Abstract: We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of $ell$ actions before deciding its course of action. While such predictive information…
Noise in Photonic Quantum Machine Learning: Models, Impacts, and Mitigation Strategies
arXiv:2603.09645v2 Announce Type: replace-cross Abstract: Photonic Quantum Machine Learning (PQML) is an emerging method to implement scalable, energy-efficient quantum information processing by combining photonic quantum computing technologies with machine learning techniques. The features of photonic technologies offer several benefits: room-temperature…
Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking
arXiv:2602.03912v2 Announce Type: replace Abstract: This paper investigates the forecasting performance of Echo State Networks (ESNs) for univariate time series forecasting using a subset of the M4 Forecasting Competition dataset. Focusing on monthly and quarterly time series with at most…
Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones
arXiv:2306.05494v5 Announce Type: replace-cross Abstract: Machine Learning (ML) has become pervasive, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy compared to traditional models in processing and classifying large volumes…
Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders
arXiv:2404.15390v3 Announce Type: replace Abstract: Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), have the potential to meet this demand by building latent representations that learn to associate uncertainties…
Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings
arXiv:2603.26798v1 Announce Type: new Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to…
Gaussian Joint Embeddings For Self-Supervised Representation Learning
arXiv:2603.26799v1 Announce Type: new Abstract: Self-supervised representation learning often relies on deterministic predictive architectures to align context and target views in latent space. While effective in many settings, such methods are limited in genuinely multi-modal inverse problems, where squared-loss prediction…
Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints
arXiv:2603.26796v1 Announce Type: new Abstract: We study the problem of routing queries to large language models (LLMs) under cost, GPU resources, and concurrency constraints. Prior per-query routing methods often fail to control batch-level cost, especially under non-uniform or adversarial batching.…