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.
Paper presents UrbanFM and UrbanPy models for inferring fine-grained urban flows.
Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source to keep consistency across the predictions. We present a multi-task learning method of generating multiple predictions for analysis via a s…
We analyze computational limits of modern Hopfield models based on pattern norms.
As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data source…
Generative profiling improves real-time task timing for varied resource contexts.
Sparse linear regression is hard to solve efficiently, even with k-sparse solutions.
ESPO optimizes LLMs for complex tasks by balancing fine-grained updates and stability.
Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement pred…
Fine-grained gap-dependent regret bounds for reinforcement learning.
We prove that the evolution of weight vectors in online gradient descent can encode arbitrary polynomial-space computations, even in very simple learning settings. Our results imply that, under weak complexity-theoretic assumptions, it is impossible to reason efficiently about the fine-grained behavior of online gradie…
FIGARO generates symbolic music with fine-grained control.
DistPre predicts traffic speeds efficiently for large networks.
New SQ lower bounds show learning mixtures of bounded covariance Gaussians is hard.
Fine-grained pretraining improves neural network's ability to learn rare features.
Generative framework learns effective, lower-dimensional models from high-dimensional data.
ECN framework improves training on noisy structured labels.
Efficient algorithms learn from coarse labels instead of fine grained ones.
New text-to-image diffusion models improve scene understanding for AI agents.
Proposes a method for weakly-supervised object localization to improve few-shot learning.
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is c…
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…
We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typically hand-craft geometric transformations, we learn stylistic and stochastic modifications leveraging state-of-the-art generative models. …
New method uses LLMs to generate detailed scientific hypotheses.
Improved investment performance with fine-grained LLM tasks.
Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction methods are incapable of capturing the subtle degreed user interests towards particular items, and the internal time-varying drifting attention …
We analyze the computational limits of LoRA for transformer models using fine-grained complexity theory.
Deep neural networks paved the way for significant improvements in image visual categorization during the last years. However, even though the tasks are highly varying, differing in complexity and difficulty, existing solutions mostly build on the same architectural decisions. This also applies to the selection of acti…
Solves dual imbalance in detecting sparse anomalies in MIL.
Deep learning models can have low bias and variance, contrary to classical theory.
Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we u…
Paper tackles cross-granularity few-shot learning with meta-embedder.
Pipelined Backpropagation trains large models without batches efficiently.
LDA improves image classification accuracy with fewer features.
Fine-grained action segmentation in long untrimmed videos is an important task for many applications such as surveillance, robotics, and human-computer interaction. To understand subtle and precise actions within a long time period, second-order information (e.g. feature covariance) or higher is reported to be effectiv…
New framework learns labels at both bag and graph levels.
NeuroDiff improves neural network equivalence verification with fine-grained approximations.
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
ECC Analyzer uses LLMs to predict stock volatility from ECCs.
We introduce a hierarchical architecture for video understanding that exploits the structure of real world actions by capturing targets at different levels of granularity. We design the model such that it first learns simpler coarse-grained tasks, and then moves on to learn more fine-grained targets. The model is train…
We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multi…
In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different aspects of the entity in question. However, training supervised models for segment-level classification requires segment labels, which may be m…
We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost. We achieve this by gating the deep-learning architecture on a fine-grained-level. Individual convolutional maps are turned on/off conditionally on features in the network. To achieve…
Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accelerators such as TPU. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as w…
Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there has been a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems…
Paper reduces neural network complexity for image classification.
Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized co…
This paper explores the computational hardness of generating latent vectors for generative models.
Improves naturalness in TTS samples using quantized VAE and auto-regressive prosody.