Learning nonparametric latent causal graphs with unknown interventions

We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity. Moreover, we do not assume the number of hidden variables is known, and we show that at most one unknown intervention per hidden variable is needed. This extends a recent line of work on learning causal representations from observations and interventions. The proofs are constructive and introduce two new graphical concepts -- imaginary subsets and isolated edges -- that may be useful in their own right. As a matter of independent interest, the proofs also involve a novel characterization of the limits of edge orientations within the equivalence class of DAGs induced by unknown interventions. These are the first results to characterize the conditions under which causal representations are identifiable without making any parametric assumptions in a general setting with unknown interventions and without faithfulness.Comment: To appear at NeurIPS 202

Review of Neural Network Algorithms

The artificial neural network is the core tool of machine learning to realize intelligence. It has shown its advantages in the fields of sound, image, sound, picture, and so on. Since entering the 21st century, the progress of science and technology and people\u27s pursuit of artificial intelligence have introduced the research of artificial neural networks into an upsurge. Firstly, this paper introduces the application background and development process of the artificial neural network in order to clarify the research context of neural networks. Five branches and related applications of single-layer perceptron, linear neural network, BP neural network, Hopfield neural network, and depth neural network are analyzed in detail. The analysis shows that the development trend of the artificial neural network is developing towards a more general, flexible, and intelligent direction. Finally, the future development of the artificial neural network in training mode, learning mode, function expansion, and technology combination has prospected

Uncovering Meanings of Embeddings via Partial Orthogonality

Machine learning tools often rely on embedding text as vectors of real numbers. In this paper, we study how the semantic structure of language is encoded in the algebraic structure of such embeddings. Specifically, we look at a notion of ``semantic independence'' capturing the idea that, e.g., ``eggplant'' and ``tomato'' are independent given ``vegetable''. Although such examples are intuitive, it is difficult to formalize such a notion of semantic independence. The key observation here is that any sensible formalization should obey a set of so-called independence axioms, and thus any algebraic encoding of this structure should also obey these axioms. This leads us naturally to use partial orthogonality as the relevant algebraic structure. We develop theory and methods that allow us to demonstrate that partial orthogonality does indeed capture semantic independence. Complementary to this, we also introduce the concept of independence preserving embeddings where embeddings preserve the conditional independence structures of a distribution, and we prove the existence of such embeddings and approximations to them

Stereo matching with temporal consistency using an upright pinhole model

Stereo vision, as a subfield of computer vision, has been researched for over 20 years. However, most research efforts have been devoted to single-frame estimation. With the rising interest in autonomous vehicles, more attention should be paid to temporal consistency within stereo matching as depth matching in this case will be used in a video context. In this thesis, temporal consistency in stereo vision will be studied in an effort to reduce time or increase accuracy by utilizing a simple upright camera model. The camera model is used for disparity prediction, which also serves as initialization for different stereo matching frameworks such as local methods and belief propagation. In particular, this thesis proposes a new algorithm based on this model and sped-up patchMatch belief propagation (SPM-BF). The results have demonstrated that the proposed method can reduce computation and convergence time.Ope

Cloud optical thickness variations during 1983-1991: Solar cycle or ENSO?

Based on a detailed analysis of the cloud data obtained by the International Satellite Cloud Climatology Project (ISCCP) in the years 1983–1991, we show that besides the reported 3% variation in global cloudiness (Svensmark and Friis-Christensen, 1997), the global mean cloud optical thickness (MCOT) also has significant variation which is out of phase with that of the global cloudiness. The combined effect of the two opposing variations may be a null effect on the cloud reflectivity. These results are consistent with the Total Ozone Mapping Spectrometer (TOMS) reflectivity measurements. The MCOT variation is further shown to be correlated with both the solar cycle and the ENSO cycle. Our present analysis cannot distinguish which of the above two provides better correlation, although independent data from the High resolution Infrared Radiation Sounder (HIRS) from 1990 to 1996 favor the solar cycle. Future data are needed to identify the true cause of these changes

Decadal evolution of the Antarctic ozone hole

Ozone column amounts obtained by the total ozone mapping spectrometer (TOMS) in the southern polar region are analyzed during late austral winter and spring (days 240–300) for 1980–1991 using area-mapping techniques and area-weighted vortex averages. The vortex here is defined using the −50 PVU (1 PVU = 1.0 × 10^(−6) K kg^(−1) m^2 s^(−1)) contour on the 500 K isentropic surface. The principal results are: (1) there is a distinct change after 1985 in the vortex-averaged column ozone depletion rate during September and October, the period of maximum ozone loss, and (2) the vortex-averaged column ozone in late August (day 240) has dropped by 70 Dobson units (DU) in a decade due to the loss in the dark and the dilution effect. The mean ozone depletion rate in the vortex between day 240 and the day of minimum vortex-averaged ozone is about 1 DU d^(−1) at the beginning of the decade, increasing to about 1.8 DU d^(−1) by 1985, and then apparently saturating thereafter. The vortex-average column ozone during September and October has declined at the rate of 11.3 DU yr^(−1) (3.8%) from 1980 to 1987 (90 DU over 8 years) and at a smaller rate of 2 DU yr^(−1) (0.9%) from 1987 to 1991 (10 DU over 5 years, excluding the anomalous year 1988). We interpret the year-to-year trend in the ozone depletion rate during the earlier part of the decade as due to the rise of anthropogenic chlorine in the atmosphere. The slower trend at the end of the decade indicates saturation of ozone depletion in the vortex interior, in that chlorine amounts in the mid-1980s were already sufficiently high to deplete most of the ozone in air within the isolated regions of the lower-stratospheric polar vortex. In subsequent years, increases in stratospheric chlorine may have enhanced wintertime chemical loss of ozone in the south polar vortex even before major losses during the Antarctic spring