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.
Service robots learn new tasks more efficiently with ISI, improving query performance and reducing training time.
Paper tackles zero-shot learning for semantic image interpretation.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
We discuss memory models which are based on tensor decompositions using latent representations of entities and events. We show how episodic memory and semantic memory can be realized and discuss how new memory traces can be generated from sensory input: Existing memories are the basis for perception and new memories ar…
This paper proposes the continuous semantic topic embedding model (CSTEM) which finds latent topic variables in documents using continuous semantic distance function between the topics and the words by means of the variational autoencoder(VAE). The semantic distance could be represented by any symmetric bell-shaped geo…
In this work, we propose a new method to integrate two recent lines of work: unsupervised induction of shallow semantics (e.g., semantic roles) and factorization of relations in text and knowledge bases. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles…
The abstract explains how word and relation representations capture semantic meaning.
Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects--such as a gene and a disease--can be related in different ways, for example…
Bayesian algorithm improves word representations using semantic taxonomy.
The paper predicts financial markets using news text and semantic network analysis.
The study categorizes knowledge graph relations and explains their embedding representations.
The paper uses differentiable rendering to generate semantic counterexamples for improving neural network robustness.
It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its …
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which s…
The paper tests deep music embeddings for semantic consistency.
Proposes a method to measure model parameter similarity for visual tasks.
Proposes a graph-based text representation for improved sentiment analysis.
Successful multimodal search and retrieval requires the automatic understanding of semantic cross-modal relations, which, however, is still an open research problem. Previous work has suggested the metrics cross-modal mutual information and semantic correlation to model and predict cross-modal semantic relations of ima…
Proposes a novel framework for multi-label text classification.
SemSentSum uses embeddings to link facts across documents efficiently.
This work benchmarks neural embeddings for link prediction in evolving knowledge graphs.
HAKE embeds entities in polar coordinates to model semantic hierarchies in knowledge graphs.
TCT learns multimodal sequence representations by translating from related sequences.
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syn…
The human face constantly conveys information, both consciously and subconsciously. However, as basic as it is for humans to visually interpret this information, it is quite a big challenge for machines. Conventional semantic facial feature recognition and analysis techniques are already in use and are based on physiol…
A new neural network model for sentiment analysis.
New approach decouples skill learning and language grounding for autonomous agents.
Enhances uncertainty estimation in medical image segmentation.
Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads o…
Recently, researchers have shown an increased interest in harnessing Twitter data for dynamic monitoring of traffic conditions. Bag-of-words representation is a common method in literature for tweet modeling and retrieving traffic information, yet it suffers from the curse of dimensionality and sparsity. To address the…
RWTNs improve NTN performance in SRL tasks.
Continuous vector representations of words and objects appear to carry surprisingly rich semantic content. In this paper, we advance both the conceptual and theoretical understanding of word embeddings in three ways. First, we ground embeddings in semantic spaces studied in cognitive-psychometric literature and introdu…
An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets s…
In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformatio…
SEMASIA provides a large dataset of latent representations for model comparison.
Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic st…
Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change t…
Tensor-based embeddings improve knowledge graph fact prediction.
We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nua…
Enhances transfer learning with semantic reasoning for robust predictions.
IdBench benchmarks semantic representations of identifiers, revealing strengths and weaknesses.
Challenge evaluates semantic code search using annotated corpus.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
SemGANs generate pixel-level accurate semantic images.
Model generates label-dependent paraphrases for NLP tasks.
Paper proposes a new framework for hypothesis testing in imaging.
In the field of Natural Language Processing (NLP), we revisit the well-known word embedding algorithm word2vec. Word embeddings identify words by vectors such that the words' distributional similarity is captured. Unexpectedly, besides semantic similarity even relational similarity has been shown to be captured in word…
Graph-RISE learns image embeddings for ultra-fine-grained semantics.