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.
Model generates label-dependent paraphrases for NLP tasks.
MARGE learns to reconstruct text by paraphrasing, achieving strong performance across multiple tasks.
Paraphrasing exemplifies the ability to abstract semantic content from surface forms. Recent work on automatic paraphrasing is dominated by methods leveraging Machine Translation (MT) as an intermediate step. This contrasts with humans, who can paraphrase without being bilingual. This work proposes to learn paraphrasin…
TGLS generates text by optimizing search results and learning from them.
Method compares sentences by cosine similarity of vector projections.
Semantic paraphrases can fool financial sentiment classifiers due to geometric shifts in model representations.
We give a explicit computation of the pointed harmonic volumes of hyperelliptic curves with Weierstrass base points, which are paraphrased into a combinatorial formula.
Word2Vec (W2V) and GloVe are popular, fast and efficient word embedding algorithms. Their embeddings are widely used and perform well on a variety of natural language processing tasks. Moreover, W2V has recently been adopted in the field of graph embedding, where it underpins several leading algorithms. However, despit…
Approach generates adversarial text examples for robustness testing.
Modeling human language learning with multi-checkpoint machine translation.
Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such a…
Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However, BiLSTM…
Many tasks in natural language understanding require learning relationships between two sequences for various tasks such as natural language inference, paraphrasing and entailment. These aforementioned tasks are similar in nature, yet they are often modeled individually. Knowledge transfer can be effective for closely …
DeepSubQE estimates translation quality for subtitles, improving on existing methods.
Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful attacks for continuous data (such as image and audio samples), generating adversarial examples for discrete structures such as text has pro…
Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a smal…
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs whil…
Recently, generative adversarial networks have gained a lot of popularity for image generation tasks. However, such models are associated with complex learning mechanisms and demand very large relevant datasets. This work borrows concepts from image and video captioning models to form an image generative framework. The…
We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want …
New approach detects sensitive info in text, outperforming previous methods.
Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to a…
A new framework explains GNN predictions by simulating graph structure and feature changes.
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound…
This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses interpretable visual concepts to explain features in middle conv-layers of a CNN. Given feature maps of a conv-layer of the CNN, the explaine…
LFD method improves text classification by making features clearer and less label-leaking.
Study on estimating Gumbel--Max watermark proportions in edited documents.
Ultra-fast search algorithm for trillion-scale corpora with semantic flexibility.
NMIXX fine-tunes embeddings for finance, outperforming general models in Korean.
Online monitoring system for safety classifiers with shift detection and conformal adaptation
The semantic map calibrates uncertainty from language model probabilities.
In this on-going work, I explore certain theoretical and empirical implications of data transformations under the PCA. In particular, I state and prove three theorems about PCA, which I paraphrase as follows: 1). PCA without discarding eigenvector rows is injective, but looses this injectivity when eigenvector rows are…
WISER detects watermarked segments in text via epidemic change-point analysis.
Paper introduces SDM for detecting LLM hallucinations, improving on entropy tests.
This paper reviews dMTL and methods for selecting auxiliary tasks.
APT-Gen generates tasks to help RL learn in hard problems.
OCEAN infers online task identities from context variables.
Deep RL multi-task learning outperforms single-task learning on new tasks.
Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between …
Proposes a new method for clustering tasks in multi-task learning.
MBML improves multi-task RL by inferring task identity from state-action pairs.
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a n…
HydaLearn dynamically adjusts task weights for better MTL performance.
New distance metric for neural architecture search reduces search space complexity.
HGNN learns augmented features for deep multi-task learning.
We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario. Although this matrix provides us critical information regarding similarity betw…
ST-MAML tackles task ambiguity in meta-learning by encoding tasks with stochastic representations.
New algorithm combines curriculum learning with HER for complex object manipulation tasks.
Transformers infer tasks from context via two modes, geometrically shaped task vectors explain their behavior.