Paper classifies transnormal systems on compact 3-manifolds.
problem Classifying transnormal systems on compact 3-manifolds.
method Establishing detailed relationships between transnormal systems of different types and their behaviors under covering maps.
result Ambient manifolds must be locally isometric to one of six standard geometries up to equivalence for CPC transnormal systems.
ContraBAR uses contrastive learning to learn Bayes-optimal policies in RL.
problem Learning optimal policies for unknown tasks sampled from a known distribution.
method Proposes ContraBAR, a meta RL algorithm using contrastive predictive coding (CPC) for belief inference.
result ContraBAR achieves comparable performance to state-of-the-art methods and is computationally efficient.
A new multi-label CPC method improves mutual information estimation and representation learning.
problem Underestimation of mutual information in contrastive predictive coding.
method Introducing a multi-label classification problem to overcome the logm bound in mutual information estimation. result The new method exceeds the logm bound and leads to better mutual information estimation and improved unsupervised representation learning. Study on discrete surfaces with constant principal curvature for nanocarbon applications.
problem Understanding discrete geometry properties of nanocarbon materials.
method Developed discrete surface theory on 3-ary oriented trees, defined discrete principal directions, constructed examples of discrete CPC surfaces.
result Construction of discrete constant principal curvature surfaces, including discrete CPC tori.
ETC improves Transformer models for long and structured inputs.
problem Scaling input length and encoding structured inputs in Transformers.
method Introduces global-local attention, relative position encodings, and CPC pre-training.
result Achieves state-of-the-art results on four natural language datasets.
Improved patent classification using fine-tuned BERT model.
problem Classifying large patent datasets efficiently and accurately.
method Fine-tuning a pre-trained BERT model on patent claims.
result Outperforms state-of-the-art methods by 20%.
We study submanifolds whose principal curvatures, counted with multiplicities, do not depend on the normal direction. Such submanifolds, which we briefly call CPC submanifolds, are always austere, hence minimal, and have constant principal curvatures. Well-known classes of examples include totally geodesic submanifolds…
We show that every spherical 2-Dupin submanifold that is not a hypersurface is conformally congruent to the standard embedding of the real, complex, quaternionic or octonionic projective plane. We also classify 2-CPC, 2-umbilical and weakly 2-umbilical submanifolds in space forms.
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their char…
In this survey article we provide an introduction to submanifold geometry in symmetric spaces of noncompact type. We focus on the construction of examples and the classification problems of homogeneous and isoparametric hypersurfaces, polar and hyperpolar actions, and homogeneous CPC submanifolds.
Optimizes stock portfolios with a constraint on correlation to reduce risk.
problem Portfolio optimization with a correlation constraint in a stochastic financial market.
method Analytical expressions for constrained subgame perfect and precommitment portfolios.
result CSGP and CPC portfolios yield lower risk than unconstrained portfolios at a small utility cost.
Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments with partial or noisy observations. In partially observable domains it is important for the representation to encode a belief state, a sufficie…
CET model uses contrastive learning to improve earnings data predictions.
problem Inaccurate stock predictions due to earnings data's irregular release and fast obsolescence.
method Contrastive Predictive Coding (CPC) for self-supervised learning of earnings data.
result CET model outperforms benchmarks in predicting stock price trends over time.
Develops methods for causal inference in longitudinal data.
problem Estimating Individual Treatment Effects (ITEs) in high-dimensional, time-varying data.
method Causal Dynamic Variational Autoencoder (CDVAE) and long-term counterfactual regression framework.
result CDVAE outperforms baselines and improves state-of-the-art models, approaching oracle performance.
Paper improves deep learning models using contrastive predictive coding for semi-supervised learning.
problem Limited labeled data in semi-supervised learning.
method Contrastive predictive coding technique to improve deep learning models with unlabeled data.
result Proposed cpc-SSL and ccpc-SSL models effectively use unlabeled data, scaling well to large datasets.
BAT benchmark for autobidding tasks in RTB auctions.
problem Lack of comprehensive datasets and benchmarks for autobidding.
method Developed a benchmark for two auction formats, implemented robust baselines.
result Provides a framework for developing and refining autobidding algorithms.
Predicting keywords performance, such as number of impressions, click-through rate (CTR), conversion rate (CVR), revenue per click (RPC), and cost per click (CPC), is critical for sponsored search in the online advertising industry. An interesting phenomenon is that, despite the size of the overall data, the data are v…
COBRA reduces modality gap in cross-modal tasks.
problem Joint embedding spaces fail to sufficiently reduce modality gap in multi-modal tasks.
method COBRA trains image and text modalities in a joint fashion using Contrastive Predictive Coding and Noise Contrastive Estimation.
result COBRA significantly reduces the modality gap and generates robust joint-embedding space.
Study investigates predictive coding models for phonemic learning.
problem Understanding how predictive coding models generalize to different languages and dataset sizes.
method Investigated Autoregressive Predictive Coding and Contrastive Predictive Coding models in phoneme discrimination tasks for two languages with varying dataset sizes.
result Contrastive Predictive Coding model converges rapidly and outperforms Autoregressive Predictive Coding on both languages.
PsychFM predicts individual gambling choices using psychological and machine learning models.
problem Predicting individual gambling choices with high precision.
method PsychFM combines machine learning and psychological theories.
result PsychFM outperforms existing models like random forest and factorization machines.
Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …
Deep network optimizes ad bidding for first-price auctions.
problem Optimizing bid prices for first-price auctions in online advertising.
method Introduced a deep distribution network for optimal bidding.
result Algorithm outperforms previous methods in terms of surplus and eCPX metrics.
In the cost per click (CPC) pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for "valueless" clicks, or so-called accid…
Study examines human factors in radiographic testing to improve inspection performance.
problem Insufficient consideration of human and organizational factors in NDT.
method CREAM method applied to analyze and model HOF on radiogram interpretation tasks.
result Model CREAM well-adapted for estimating HOF impact on NDT performances.