Understanding the foundational distortion of digital audio from first principles, with worked examples and visual intuition The post Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms appeared first on Towards Data Science.
Deep Reinforcement Learning systems are highly sensitive to the learning rate (LR), and selecting stable and performant training runs often requires extensive hyperparameter search. In Proximal Policy Optimization (PPO) actor–critic methods, small LR values lead to slow convergence, whereas large LR values may induce instability or collapse. We analyse this phenomenon from the behavior of the hidden neurons in the network using the Overfitting-Underfitting Indicator (OUI), a metric that quantifies the balance of binary activation patterns over a […]
arXiv:2502.02132v3 Announce Type: replace-cross Abstract: In modern optimization methods used in deep learning, each update depends on the history of previous iterations, often referred to as memory, and this dependence decays fast as the iterates go further into the past. For example, gradient descent with momentum has exponentially decaying memory through exponentially averaged past gradients. We introduce a general technique for identifying a memoryless algorithm that approximates an optimization algorithm with memory. It is obtained by replacing all […]
arXiv:2601.14356v1 Announce Type: new Abstract: Audio restoration consists in inverting degradations of a digital audio signal to recover what would have been the pristine quality signal before the degradation occurred. This is valuable in contexts such as archives of music recordings, particularly those of precious historical value, for which a clean version may have been lost or simply does not exist. Recent work applied generative models to audio restoration, showing promising improvement over previous methods, and opening the […]
arXiv:2601.10854v1 Announce Type: new Abstract: Human action recognition has become an important research focus in computer vision due to the wide range of applications where it is used. 3D Resnet-based CNN models, particularly MC3, R3D, and R(2+1)D, have different convolutional filters to extract spatiotemporal features. This paper investigates the impact of reducing the captured knowledge from temporal data, while increasing the resolution of the frames. To establish this experiment, we created similar designs to the three originals, but […]
arXiv:2602.16866v1 Announce Type: new Abstract: This letter investigates properties of the second-order cone relaxation of the optimal power flow (OPF) problem, with emphasis on relaxation tightness, nodal voltage angles recovery, and alternating-current-OPF feasibility in meshed networks. The theoretical discussion is supported by numerical experiments on standard IEEE test cases. Implications for power system planning are briefly outlined.
Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a single blade just 4-5 millimeters wide. To help figure skaters land quadruple axels, Salchows, Lutzes, and maybe even the elusive quintuple without looking the least bit stressed, Jerry Lu MFin ’24 developed an optical tracking system called OOFSkate that uses artificial intelligence to analyze video of a figure skater’s jump and make recommendations on how […]
arXiv:2602.02625v1 Announce Type: new Abstract: Agentic AI systems increasingly operate in shared social environments where they exchange information, instructions, and behavioral cues. However, little empirical evidence exists on how such agents regulate one another in the absence of human participants or centralized moderation. In this work, we present an empirical analysis of OpenClaw agents interacting on Moltbook, an agent-only social network. Analyzing 39,026 posts and 5,712 comments produced by 14,490 agents, we quantify the prevalence of action-inducing instruction […]
This post was co-authored with Benjamin Kleppe, Machine Learning Engineering Lead at bunq. The integration of agentic AI is transforming the banking industry, marking a significant shift from traditional customer service systems. Agentic AI demonstrates autonomous decision-making capabilities in complex financial environments, enabling banks to provide round-the-clock multilingual support, process transactions, and deliver personalized financial insights at scale. bunq is Europe’s second-largest neobank, built to make life easy for people and businesses who live an international lifestyle. Founded […]
Hi everyone, we’re a group of students working on a reinforcement learning project and we’re honestly pretty stuck. We’ve been trying to solve this for weeks and feel like we’re missing some fundamental understanding. Below are the main problems we’re facing: Problem Setup We train an agent to pick up items and deliver them to a target on a small grid. We use PPO as we thought it is most advanced We split the reward 50/50 between pickup […]