Learning with Single View Co-training and Marginalized Dropout

The generalization properties of most existing machine learning techniques are predicated on the assumptions that 1) a sufficiently large quantity of training data is available; 2) the training and testing data come from some common distribution. Although these assumptions are often met in practice, there are also many scenarios in which training data from the relevant distribution is insufficient. We focus on making use of additional data, which is readily available or can be obtained easily but comes from a different distribution than the testing data, to aid learning. We present five learning scenarios, depending on how the distribution we used to sample the additional training data differs from the testing distribution: 1) learning with weak supervision; 2) domain adaptation; 3) learning from multiple domains; 4) learning from corrupted data; 5) learning with partial supervision. We introduce two strategies and manifest them in five ways to cope with the difference between the training and testing distribution. The first strategy, which gives rise to Pseudo Multi-view Co-training: PMC) and Co-training for Domain Adaptation: CODA), is inspired by the co-training algorithm for multi-view data. PMC generalizes co-training to the more common single view data and allows us to learn from weakly labeled data retrieved free from the web. CODA integrates PMC with an another feature selection component to address the feature incompatibility between domains for domain adaptation. PMC and CODA are evaluated on a variety of real datasets, and both yield record performance. The second strategy marginalized dropout leads to marginalized Stacked Denoising Autoencoders: mSDA), Marginalized Corrupted Features: MCF) and FastTag: FastTag). mSDA diminishes the difference between distributions associated with different domains by learning a new representation through marginalized corruption and reconstruciton. MCF learns from a known distribution which is created by corrupting a small set of training data, and improves robustness of learned classifiers by training on ``infinitely\u27\u27 many data sampled from the distribution. FastTag applies marginalized dropout to the output of partially labeled data to recover missing labels for multi-label tasks. These three algorithms not only achieve the state-of-art performance in various tasks, but also deliver orders of magnitude speed up at training and testing comparing to competing algorithms

Analysis on the Way of Cultivating Talents of Vocational Education to Develop Entrepreneurial Economy

The development of entrepreneurial economy has brought new vitality to China as well as raised higher requirements for talents training. In order to meet the needs of the entrepreneurial economy for high-level talents, vocational education should promote entrepreneurship education, develop effective ways for school-enterprise cooperation, expand communication channels with other educational types, and improve teaching and evaluation methods, so as to meet the challenges brought by the transformation and upgrading of industrial economy

RESPONSE LATENCY OF EXTERNAL AND CENTRAL NUCLEI IN THE AWAKE MARMOSET INFERIOR COLLICULUS

The response latency of neurons has been used to represent properties of acoustic stimuli. However, the latency also indicates the characteristic of neurons themselves. The role of the inferior colliculus (IC) is unclear, especially the external nucleus (ICX). Latency studies may provide a clue as to how neurons process stimuli. Single unit recordings of responses to tones in the central nucleus of the IC (ICC) and ICX were utilized. The dependence of latencies on the units tuning properties, tone frequencies, and attenuations were analyzed. In particular, we computed a weighted average latency across frequencies at a variety of sound levels and the first spike latency at neuron’s best frequency. Results showed that response latencies were shorter at higher sound levels in both the ICC and ICX. ICX neurons tend to have longer latencies than ICC neurons. Moreover, there was no discernable relationship between frequency selectivity and latency in ICX units