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.
MILCCI integrates labels across categories for better understanding of multi-trial data.
RDLI integrates domain logic and context grounding to detect crypto anomalies under scarce labels.
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deploymen…
Enhances LDL by integrating distance and directional information for more robust label feature representation.
Paper improves image classification accuracy with a new Noise Modeling Network.
A new neural network layer integrates graph learning into classification tasks.
It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypoth…
Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly. In this paper, we t…
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self train…
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performa…
Study enhances classifier robustness against noisy labels.
RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
Paper improves short text clustering by integrating semantic relationships into Optimal Transport.
Paper introduces SUEL model for integrating predictors without labeled data.
Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true…
Integrates multiple datasets to solve open set crowdsourcing problems.
Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels, e.g., a user may…
Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event dete…
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semanti…
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this pap…
Accelerates DNN robustness verification with target labels.
Paper proposes efficient method to calculate Fisher-Bingham distribution normalizing constant.
Classifies actions of tori on manifolds up to diffeomorphisms.
We consider the learning from noisy labels (NL) problem which emerges in many real-world applications. In addition to the widely-studied synthetic noise in the NL literature, we also consider the pseudo labels in semi-supervised learning (Semi-SL) as a special case of NL. For both types of noise, we argue that the gene…
Enhances image classification by integrating semantic hierarchy into CNN models.
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough …
The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node features and graph topologic information to build learning models. However, as for multi-label learning tasks, the supervision part of GCN simply minimizes the cross-entropy loss …
GUST framework improves self-training by estimating node uncertainty and generating pseudo-labels.
The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks. Though a small-scale program in terms of size, the package is, to the best of the authors' knowledge, the first of its kind which prov…
We consider the problem of naming objects in complex, natural scenes containing widely varying object appearance and subtly different names. Informed by cognitive research, we propose an approach based on sharing context based object hypotheses between visual and lexical spaces. To this end, we present the Visual Seman…
HMS-BERT detects cyberbullying in multiple languages and labels.
Bayesian method improves deep learning for noisy EEG seizure detection.
Classifies geodesic flows on projective plane with potential field.
OpinionRank uses graph-based ranking to improve unreliable crowdsourced labels.
Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals. While existing work mainly focuses on utilizing a certain type of weak supervision, we present a probabilistic framework, learning from indirect observations, for learn…
INN method refines clean labeled data from noisy labels.
Improves SSL with doubly robust estimation of unlabeled class distribution.
The graph-based semi-supervised label propagation algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, available methods typically calculate the weights…
SPREV simplifies visualization of complex labeled datasets.
New method builds robust trees from noisy data.
Paper tackles active learning under human label variation, proposing a new framework.
Paper tackles causal inference with partially labeled data, introducing robust methods.
A new framework for semi-supervised learning using pseudo-representation labeling.
SNS-GAN integrates class labels into generative models for images and time series.
A new AI framework optimizes expert labeling of unlabeled data.
Bayesian Pseudo Label Selection reduces overfitting in semi-supervised learning.