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.
BERT learns claim descriptions to identify patent novelty.
Improved AI patent classifier measures U.S. and China's AI patenting.
In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification. When applied to large datasets of over two millions patents, our approach outperforms the state of the art by an approach using CNN with word embeddings. In addition, we focus on patent claims without other parts in …
Patent lawsuits are costly and time-consuming. An ability to forecast a patent litigation and time to litigation allows companies to better allocate budget and time in managing their patent portfolios. We develop predictive models for estimating the likelihood of litigation for patents and the expected time to litigati…
Estimates yearly improvement rates for nearly all technologies using US patent data.
In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation. Patent claim language itself has rarely been explored in the past and poses a unique ch…
Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional inten…
Study predicts startup outcomes like funding, patenting, IPOs using machine learning.
Improved antibody humanness prediction using patent data.
This paper explores using NFTs for patents, offering a framework and addressing challenges.
Mass algorithm predicts M&A deals from patent data.
The paper models network formation using mixed logit models.
Paper introduces ML for rare-event prediction in patent quality estimation.
Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), t…
We describe a number of devices for pulling candy, called taffy pullers,that are related to pseudo-Anosov maps of punctured spheres. Though the mathematical connection has long been known for the two most common taffy puller models, we unearth a rich variety of early designs from the patent literature, and introduce a …
Proposes a novel method for detecting novelty in multi-modal data.
Deep learning detects novel changes in time series data.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, dat…
This paper introduces CENIE to quantify environment novelty for better UED.
Develops a deep learning framework to predict future tech directions for high-tech companies.
Since datasets with annotation for novelty at the document and/or word level are not easily available, we present a simulation framework that allows us to create different textual datasets in which we control the way novelty occurs. We also present a benchmark of existing methods for novelty detection in textual data s…
Generatability in metric spaces studied with novel novelty parameters.
DREAM model improves computational efficiency for non-linear effects in relational event models.
As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have sugge…
This work introduces a novel method to evaluate generative model novelty.
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders…
We study the relationship between firms' performance and their technological portfolios using tools borrowed from the complexity science. In particular, we ask whether the accumulation of knowledge and capabilities related to a coherent set of technologies leads firms to experience advantages in terms of productive eff…
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
Novelty search in low-dimensional space improves sample efficiency in exploration tasks.
News novelty predicts negative stock market returns.
This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching c…
New algorithm classifies and generates genomic sequences using RG-flow categorifier.
Paper proposes AdaDetect for FDR-controlled novelty detection.
The paper analyzes tech specialization and diversification at various scales.
Out of the companies, Dolby is the company with the best overall financial and operation health. According to the table that accounted its financial statements for the past three years, Dolby has stable profit margins that generates a revenue in the billions, the only company in ten figures. Corporate competition to ga…
Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to the invertibility, such models can score unseen data samples by computing their exact likelihood under the learned distribution. This makes fl…
Proposes a context-aware approach to deep autoencoder novelty detection.
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern…
We perform an optimal localization of asymptotically flat initial data sets and construct data that have positive ADM mass but are exactly trivial outside a cone of arbitrarily small aperture. The gluing scheme that we develop allows to produce a new class of -body solutions for the Einstein equation, which patently…
Higher-order optimization problems naturally appear when investigating the effects of a patent with finite length, as in the pioneering work of Futagami and Iwaisako (2007). In this paper, we establish the Euler equations and transversality conditions necessary for analyzing such higher-order optimization problems. We …
Proposes UICR to improve novelty in recommendation systems without sacrificing relevance.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
OCmst detects anomalies using CNN features and MSTs.
NoLBERT avoids lookback and lookahead biases for better econometric inference.
New machine learning model faster, more accurate, and can identify hard-to-classify samples.
The paper develops methods for novelty detection on path space using signature-based statistics.
A new method for student-initiated action advice using novelty detection.
Decentralized detection avoids sharing data, controls false discoveries.