A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Energy savings for DNN inference on resource-constrained devices.
Deep learning detects inaccurate smart meters for resource savings.
Prunes neural networks at initialization to save resources, achieving high accuracy.
Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. However, graph attention operators (GAOs) consume exces…
To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …
Post-training quantization saves resources for neural networks.
Researchers save memory on MCUs by reordering neural network operators.
A new optimizer saves significant shots in quantum machine learning.
Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…
E2-Train reduces training energy by 80%+ for state-of-the-art CNNs.
Spectrum selectively trains LLMs based on SNR to save resources.
Paper proposes a new binary quantization method for faster DNN inference.
We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the pr…
Pseudo rehearsal uses non-photo-realistic images to save resources without sacrificing performance.
Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly comput…
GP-TS optimizes TLM pre-training hyperparameters efficiently.
EagerNet detects network attacks quickly with less resources.
Study evaluates feature selection methods for emotion recognition in resource-constrained settings.
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE). Such LNEs, however, can be arbitrarily far from an actual Nash equilibrium (NE), which implies that there are no guarantees on the quality of the found…
This work reduces computation cost for on-device CNN training.
New layer sparsity concept improves neural networks.
Paper presents efficient algorithms for convolutional neural networks using Winograd minimal filtering.
Risk control improves EENNs to make faster predictions without sacrificing accuracy.
Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow sigmoid-type neural networks that achieve 100% accuracy in classification for datasets following a linear separability condition. The separab…
Every year, 3 million newborns die within the first month of life. Birth asphyxia and other breathing-related conditions are a leading cause of mortality during the neonatal phase. Current diagnostic methods are too sophisticated in terms of equipment, required expertise, and general logistics. Consequently, early dete…
Paper defines hyperparameter importance for efficient tuning.
Study user engagement in mobile health apps for health workers in resource-poor settings.
Optimizes network sampling for efficient community detection.
SAM improves deep learning tasks by promoting balancedness, reducing outlier impact.
EC2T creates sparse and ternary neural networks for resource-constrained devices.
Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days. However, in spite of hundreds of papers on the topic being published every year, current theoretical understanding and practical implementations of such tasks, in particular of clustering, is very rudi…
Entropy-based model for hierarchical learning from multiscale data.
Paper proposes a data augmentation method for LLM-generated data in market research.
Argentum is a crypto coin for saving and investment in unstable countries.
This paper analyzes the equilibrium distribution of wealth in an economy where firms' productivities are subject to idiosyncratic shocks, returns on factors are determined in competitive markets, dynasties have linear consumption functions and government imposes taxes on capital and labour incomes and equally redistrib…
Handling the tremendous amount of network data, produced by the explosive growth of mobile traffic volume, is becoming of main priority to achieve desired performance targets efficiently. Opportunistic communication such as FloatingContent (FC), can be used to offload part of the cellular traffic volume to vehicular-to…
Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time. This disjunction makes the state-of-art deep learning algorithms, i.e. CNN (Convolutional Neural Networ…
MoDeGPT compresses large language models without accuracy loss, saving 98% compute costs.
Prize linked savings accounts provide a return in the form of randomly chosen accounts receiving large cash prizes, in lieu of a guaranteed and uniform interest rate. This model became legal for American national banks upon bipartisan passage of the American Savings Promotion Act in December 2014, and many states have …
Study on pooled annuity funds and how initial savings affect income stability.
A new method for sparse regression models using graph structure.
The effects of saving and spending patterns on holding time distribution of money are investigated based on the ideal gas-like models. We show the steady-state distribution obeys an exponential law when the saving factor is set uniformly, and a power law when the saving factor is set diversely. The power distribution c…
Interleaved RNNs detect fraud without costly features.
We analyze the ideal gas like models of markets and review the different cases where a `savings' factor changes the nature and shape of the distribution of wealth. These models can produce similar distribution of wealth as observed across varied economies. We present a more realistic model where the saving factor can v…
Paper proposes an efficient method for bounding box annotation in object detection.
We review a simple model of closed economy, where the economic agents make money transactions and a saving criterion is present. We observe the Gibbs distribution for zero saving propensity, and non-Gibbs distributions otherwise. While the exact solution in the case of zero saving propensity is already known to be give…
In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints. We take a joint power and bandwidth allocation problem as an example, which mini…
We consider a simple model of a closed economic system where the total money is conserved and the number of economic agents is fixed. In analogy to statistical systems in equilibrium, money and the average money per economic agent are equivalent to energy and temperature, respectively. We investigate the effect of the …