arXiv research

ReQuestNet simplifies 5G channel estimation with a unified model.

Meta-DRL improves resource allocation in O-RAN networks.

Optimizes network sampling for efficient community detection.

In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in o…

Unified framework for accelerating DNNs on resource-limited platforms.

Paper develops security model and pricing for stable digital currency in quantum blockchain network.

Wide-AdGraph detects ads and trackers using a graph of resource requests.

Automatically balances blockchain network resources to boost market efficiency.

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

SympFormer accelerates attention blocks using inertial dynamics on density spaces.

HydroNets use river structure to improve hydrologic predictions.

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all the UEs via optimizing the user association, reso…

New algorithm optimizes online network resource allocation with long-term constraints.

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de…

This paper examines MEV attacks in dynamic AMMs and proposes new protections.

Quantum advantage in derivative pricing requires 8k qubits and 54M T-depth.

Study on neuron dynamics for XOR classification with zero-margin.

Continuous-depth Evoformer reduces protein folding prediction time and resource usage.

A spring-block chain placed on a running conveyor belt is considered for modeling stylized facts observed in the dynamics of stock indexes. Individual stocks are modeled by the blocks, while the stock-stock correlations are introduced via simple elastic forces acting in the springs. The dragging effect of the moving be…

Proposes a new framework for resource-limited recommendation.

Optimal online learning for joint pricing and resource allocation.

RL optimizes resource allocation in MG by balancing experience and exploration.

Study optimizes resource allocation in noisy systems for better control.

Recently, with convolutional neural networks gaining significant achievements in many challenging machine learning fields, hand-crafted neural networks no longer satisfy our requirements as designing a network will cost a lot, and automatically generating architectures has attracted increasingly more attention and focu…

This study uses OPE methods to quickly assess auction policies.

The paper proposes an online algorithm for network resource allocation with reduced costs.

DaringFed incentivizes clients in OFL with dynamic rewards under TII.

Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of…

Improved spectral clustering guarantees for dynamic stochastic block models.

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

Adaptive RL optimizes testing resource allocation for dynamic software environments.

Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potentially require fewer communication rounds to converge. However, th…

Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and De…

Study community detection in multi-view data with various types of information.

Optimizes resource allocation in a network with random job requests.

dynoNet learns dynamical systems using linear operators.

PASHA optimizes model tuning for large datasets with limited resources.

Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite the success, the huge number of parameters blocks its deployment to situations with light computing resources. Researchers resort to the re…

Finding the best neural network architecture requires significant time, resources, and human expertise. These challenges are partially addressed by neural architecture search (NAS) which is able to find the best convolutional layer or cell that is then used as a building block for the network. However, once a good buil…

New MBL hidden Born machine learns various tasks.

This paper analyzes the profitability of selfish mining on blockchain, considering the risk of ruin.

We study the problem of computing the matrix exponential of a block triangular matrix in a peculiar way: Block column by block column, from left to right. The need for such an evaluation scheme arises naturally in the context of option pricing in polynomial diffusion models. In this setting a discretization process pro…

We present a simplified model for the exploitation of finite resources by interacting agents, where each agent receives a random fraction of the available resources. An extremal dynamics ensures that the poorest agent has a chance to change its economic welfare. After a long transient, the system self-organizes into a …

New algorithm tackles dynamic assortment optimization with knapsack constraints.

We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting. This forms part of a United Nations effort using keyword spotting to support humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We use 1920 isolated keywords (40 types, 34 minutes)…

Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a way to reduce the memory and computation footprint of these models has seen significant research attention in recent years. In this paper, w…

As most natural resources, fisheries are affected by random disturbances. The evolution of such resources may be modelled by a succession of deterministic process and random perturbations on biomass and/or growth rate at random times. We analyze the impact of the characteristics of the perturbations on the management o…

A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built. These include hierarchical models for variances of other variables and many nonlinear models. The underlying variational B…