8,650 research outputs found
Prototypical Contrastive Learning of Unsupervised Representations
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised
representation learning method that addresses the fundamental limitations of
instance-wise contrastive learning. PCL not only learns low-level features for
the task of instance discrimination, but more importantly, it implicitly
encodes semantic structures of the data into the learned embedding space.
Specifically, we introduce prototypes as latent variables to help find the
maximum-likelihood estimation of the network parameters in an
Expectation-Maximization framework. We iteratively perform E-step as finding
the distribution of prototypes via clustering and M-step as optimizing the
network via contrastive learning. We propose ProtoNCE loss, a generalized
version of the InfoNCE loss for contrastive learning, which encourages
representations to be closer to their assigned prototypes. PCL outperforms
state-of-the-art instance-wise contrastive learning methods on multiple
benchmarks with substantial improvement in low-resource transfer learning. Code
and pretrained models are available at https://github.com/salesforce/PCL
Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach
To cope with the accelerating pace of technological changes, talents are
urged to add and refresh their skills for staying in active and gainful
employment. This raises a natural question: what are the right skills to learn?
Indeed, it is a nontrivial task to measure the popularity of job skills due to
the diversified criteria of jobs and the complicated connections within job
skills. To that end, in this paper, we propose a data driven approach for
modeling the popularity of job skills based on the analysis of large-scale
recruitment data. Specifically, we first build a job skill network by exploring
a large corpus of job postings. Then, we develop a novel Skill Popularity based
Topic Model (SPTM) for modeling the generation of the skill network. In
particular, SPTM can integrate different criteria of jobs (e.g., salary levels,
company size) as well as the latent connections within skills, thus we can
effectively rank the job skills based on their multi-faceted popularity.
Extensive experiments on real-world recruitment data validate the effectiveness
of SPTM for measuring the popularity of job skills, and also reveal some
interesting rules, such as the popular job skills which lead to high-paid
employment.Comment: 8 pages, 14 figures, AAAI 201
Empirical study on the efficiency of the stock index futures market from the information and functional perspectives–empirical evidence from China
This paper studies the effectiveness of the CSI 300 index futures markets from the perspective of information efficiency and function efficiency and examines the nonlinear dynamic characteristics of efficiency by using nonparametric methods. For information effectiveness, we find that the price of stock index futures follows a random walk. For function effectiveness, the results show that (1) the average optimal hedge ratio is 0.8702, and the average effective level reaches 86.11%. (2) The error correction mechanism is only supported by stock index futures. The error correction effect only exists in the extreme regime (only 6% of the total observed value). Most of the time (94%), both prices are subject to random walk process. There is no arbitrage trade between futures and spots. (3) Both linear and nonlinear leadership are observed in stock index futures. The nonlinear leadership is mainly reflected in stock index futures. Both leadership types are influenced by institutional changes and significant financial events and evolve over time, which indicates that stock index futures cannot play the dominant role in price discovery. In sum, we conclude that the CSI 300 stock index futures market is effective, despite the flaws in price discovery
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?
Robustness, the ability of models to maintain performance in the face of
perturbations, is critical for developing reliable NLP systems. Recent studies
have shown promising results in improving the robustness of models through
adversarial training and data augmentation. However, in machine translation,
most of these studies have focused on bilingual machine translation with a
single translation direction. In this paper, we investigate the transferability
of robustness across different languages in multilingual neural machine
translation. We propose a robustness transfer analysis protocol and conduct a
series of experiments. In particular, we use character-, word-, and multi-level
noises to attack the specific translation direction of the multilingual neural
machine translation model and evaluate the robustness of other translation
directions. Our findings demonstrate that the robustness gained in one
translation direction can indeed transfer to other translation directions.
Additionally, we empirically find scenarios where robustness to character-level
noise and word-level noise is more likely to transfer
Human Mobility Trends during the COVID-19 Pandemic in the United States
In March of this year, COVID-19 was declared a pandemic and it continues to
threaten public health. This global health crisis imposes limitations on daily
movements, which have deteriorated every sector in our society. Understanding
public reactions to the virus and the non-pharmaceutical interventions should
be of great help to fight COVID-19 in a strategic way. We aim to provide
tangible evidence of the human mobility trends by comparing the day-by-day
variations across the U.S. Large-scale public mobility at an aggregated level
is observed by leveraging mobile device location data and the measures related
to social distancing. Our study captures spatial and temporal heterogeneity as
well as the sociodemographic variations regarding the pandemic propagation and
the non-pharmaceutical interventions. All mobility metrics adapted capture
decreased public movements after the national emergency declaration. The
population staying home has increased in all states and becomes more stable
after the stay-at-home order with a smaller range of fluctuation. There exists
overall mobility heterogeneity between the income or population density groups.
The public had been taking active responses, voluntarily staying home more, to
the in-state confirmed cases while the stay-at-home orders stabilize the
variations. The study suggests that the public mobility trends conform with the
government message urging to stay home. We anticipate our data-driven analysis
offers integrated perspectives and serves as evidence to raise public awareness
and, consequently, reinforce the importance of social distancing while
assisting policymakers.Comment: 11 pages, 9 figure
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