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.
A new method reduces task interference in model merging.
Paper proposes ADC framework to reduce ViT SL training communication overhead.
G-CREWE efficiently aligns large networks using node embeddings and compression.
Proposes a new linearity-based neural network compression method.
The paper defines and studies discrete p-density and compression-radius profiles of lattice knots.
Deep neural network compression techniques such as pruning and weight tensor decomposition usually require fine-tuning to recover the prediction accuracy when the compression ratio is high. However, conventional fine-tuning suffers from the requirement of a large training set and the time-consuming training procedure. …
A new method reduces communication costs in distributed learning.
The paper connects disentanglement to manifold charts and commutativity.
LEWIS merges LLMs without training, improving performance on specific tasks.
Modern vehicles are equipped with increasingly complex sensors. These sensors generate large volumes of data that provide opportunities for modeling and analysis. Here, we are interested in exploiting this data to learn aspects of behaviors and the road network associated with individual drivers. Our dataset is collect…
Study shows training duration impacts model merging quality, suggesting joint selection of duration and method.
Study shows training duration affects model merging quality, suggesting joint selection of duration and method.
EpiMer merges models by solving Fréchet mean on a Riemannian manifold.
New Bayesian method improves Pareto front estimation in multitask finetuning.
Deep reinforcement learning (DRL) on Markov decision processes (MDPs) with continuous action spaces is often approached by directly training parametric policies along the direction of estimated policy gradients (PGs). Previous research revealed that the performance of these PG algorithms depends heavily on the bias-var…
Study on merging predictors in causal and anticausal directions using CMAXENT.
In this paper, a similarity-driven cluster merging method is proposed for unsuper-vised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-…
New method merges MCMC samples without distributional assumptions.
Securely evaluates the benefits of merging datasets for causal estimation.
A new method merges neural networks using CCA to improve model performance.
This paper reverses a construction by merging boundary critical points into an interior one.
Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art technique for training large-scale kernelized support vector machines. The budget constraint is maintained incrementally by merging two points whenever the pre-defined budget is exceeded. The process of finding suitable merge partners is costly; it can a…
NAMEx merges experts using Nash bargaining for improved performance.
Efficiently estimates longitudinal networks by merging sparse networks.
Discrete knot theory models use lattice-filtered graphs to detect merging knot components.
Boosting strategies for merging vs. ensembling studies analyzed.
Method constructs finance LLMs without instruction data using pretraining and model merging.
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans, by contrast, can easily detect such errors by acquiring intuition for the correct morphology of objects. Biological neurons have complicated…
Single global merging boosts decentralized learning performance.
COMET is a single-pass MapReduce algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed blocks of data and merges them into a mega-ensemble. This approach is appropriate when learning from massive-scale data that is too large to fit on a single machine. To get the best acc…
Gradient boosted decision trees (GBDT) is the leading algorithm for many commercial and academic data applications. We give a deep analysis of this algorithm, especially the histogram technique, which is a basis for the regulized distribution with compact support. We present three new modifications. 1) Share memory tec…
DPSM clusters nodes in data and graph spaces via density propagation and subcluster merging.
Bayesian Federated Inference improves survival model analysis without merging data.
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of coop…
New methods merge discrete gradient fields from patches to correct errors.
The hierarchical Dirichlet process (HDP) has become an important Bayesian nonparametric model for grouped data, such as document collections. The HDP is used to construct a flexible mixed-membership model where the number of components is determined by the data. As for most Bayesian nonparametric models, exact posterio…
The article improves the display of acceptable exchange ratios for merging companies.
NetFuse merges different DNN models with varying weights for faster inference.
The MAXENT principle helps merge datasets to infer causal effects.
A critical decision point when training predictors using multiple studies is whether studies should be combined or treated separately. We compare two multi-study prediction approaches in the presence of potential heterogeneity in predictor-outcome relationships across datasets: 1) merging all of the datasets and traini…
Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and popular variants of MCMC for problems when an MCMC state consists of an unknown number of components. It is well known that state-of-the-art methods for split-merge MCMC do not scale well. Strategies for rapid mixing requires smart and informative …
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
Merging datasets is a key operation for data analytics. A frequent requirement for merging is joining across columns that have different surface forms for the same entity (e.g., the name of a person might be represented as "Douglas Adams" or "Adams, Douglas"). Similarly, ontology alignment can require recognizing disti…
Improved sampling for network community detection.
Locally adaptive clustering for tree delineation.
We propose a greedy mixture reduction algorithm which is capable of pruning mixture components as well as merging them based on the Kullback-Leibler divergence (KLD). The algorithm is distinct from the well-known Runnalls' KLD based method since it is not restricted to merging operations. The capability of pruning (in …
To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computational complexity. Exploiting variable augmentation, REINFORCE, and reparameterization, the ARM estimator achieves adaptive variance reducti…
If a rectangular diagram represents the trivial knot, then it can be deformed into the trivial rectangular diagram with only four edges by a finite sequence of merge operations and exchange operations, without increasing the number of edges, which was shown by I. A. Dynnikov. Using this, Henrich and Kauffman gave an up…