End-to-end optimization has achieved state-of-the-art performance on many specific problems, but there is no straight-forward way to combine pretrained models for new problems. Here, we explore improving modularity by learning a post-hoc interface between two existing models to solve a new task. Specifically, we take inspiration from neural machine translation, and cast the challenging problem of cross-modal domain transfer as unsupervised translation between the latent spaces of pretrained deep generative models. By abstracting away the data representation, we demonstrate that it is possible to transfer across different modalities (e.g., image-to-audio) and even different types of generative models (e.g., VAE-to-GAN). We compare to state-of-the-art techniques and find that a straight-forward variational autoencoder is able to best bridge the two generative models through learning a shared latent space. We can further impose supervised alignment of attributes in both domains with a classifier in the shared latent space. Through qualitative and quantitative evaluations, we demonstrate that locality and semantic alignment are preserved through the transfer process, as indicated by high transfer accuracies and smooth interpolations within a class. Finally, we show this modular structure speeds up training of new interface models by several orders of magnitude by decoupling it from expensive retraining of base generative models.

President Donald Trump released a splashy new plan for American artificial intelligence last week. High on enthusiasm, low on details, its goal is to ramp up the rate of progress in AI research so the United States won't get outpaced by countries like China. Experts had been warning for months that under Trump, the US hasn't been doing enough to maintain its competitive edge. Now, it seems, Trump has finally got the memo. His executive order, signed February 11, promises to "drive technological breakthroughs ... in order to promote scientific discovery, economic competitiveness, and national security."

Hear the discussion from copywriter, voice actor and marketer Rew Shearer as he talks through the why, how, and watch out! of chatbot personality in this short live webinar co-hosted by Chief Conversologist Jam Mayer. One of the hardest elements of creating a chatbot is personality. Building a chatbot can be easy. But getting the conversation right is hard. Do you even need a personality for your chatbot – and why?

Predicting the direction of assets have been an active area of study and a difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods is one of them showing results better than a single supervised method. In this paper, we have used generative and discriminative classifiers to create the stack, particularly 3 generative and 9 discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies. Features used are technical indicators used are not limited to trend, momentum, volume, volatility indicators, and sentiment analysis has also been used to gain useful insight combined with the above features. For Cross-validation, Purged Walk forward cross-validation has been used. In terms of accuracy, we have done a comparative analysis of the performance of Ensemble method with Stacking and Ensemble method with blending. We have also developed a methodology for combined features importance for the stacked model. Important indicators are also identified based on feature importance.

In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions. We are motivated by the optimisation of physical and industrial processes where, for some input ranges, small and unavoidable variations in inputs lead to unacceptably large variation in outputs. Our approach uses multiple gradient Gaussian Process models to estimate the probability that worst-case output variation for specified input perturbation exceeded the desired maxima, and these probabilities are then used to (a) guide the optimisation process toward solutions satisfying our stability criteria and (b) post-filter results to find the best stable solution. We exhibit our algorithm on synthetic and real-world problems and demonstrate that it is able to effectively find stable maxima.

We revisit the proof by Qin et al. (2014) of bounded regret of the C$^2$UCB contextual combinatorial bandit. We demonstrate an error in the proof of volumetric expansion of the moment matrix, used in upper bounding a function of context vector norms. We prove a relaxed inequality that yields the originally-stated regret bound.

Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human factors. Therefore, this paper proposes a novel semi-supervised learning paradigm that can handle both label insufficiency and label inaccuracy. To address label insufficiency, we use a graph to bridge the data points so that the label information can be propagated from the scarce labeled examples to unlabeled examples along the graph edges. To address label inaccuracy, Graph Trend Filtering (GTF) and Smooth Eigenbase Pursuit (SEP) are adopted to filter out the initial noisy labels. GTF penalizes the l_0 norm of label difference between connected examples in the graph and exhibits better local adaptivity than the traditional l_2 norm-based Laplacian smoother. SEP reconstructs the correct labels by emphasizing the leading eigenvectors of Laplacian matrix associated with small eigenvalues, as these eigenvectors reflect real label smoothness and carry rich class separation cues. We term our algorithm as `Semi-supervised learning under Inadequate and Incorrect Supervision' (SIIS). Thorough experimental results on image classification, text categorization, and speech recognition demonstrate that our SIIS is effective in label error correction, leading to superior performance to the state-of-the-art methods in the presence of label noise and label scarcity.

Deep neural networks are usually trained with stochastic gradient descent (SGD), which optimizes $\theta\in\mathbb{R}^D$ parameters to minimize objective function using very rough approximations of gradient, only averaging to the real gradient. Standard approaches like momentum or ADAM only consider single direction, and do not try to model distance from extremum - neglecting valuable information from calculated gradients. It can be improved by second order methods, but they are costly, need inverse of Hessian - problematic especially in the stochastic setting. Proposed general framework should overcome these difficulties by directly evolving local second order parametrization in $d\ll D$ directions: as $\sum_{i=1}^d \lambda_{i}(\theta\cdot v_{i}-p_{i})^2$ modelling local information we are interested in, and relatively simple to update for better agreement with calculated gradients. It allows for $\theta$ update by simultaneously attracting toward modelled directional minima $(\lambda_i>0)$, and repulsing from maxima $(\lambda_i<0)$, correspondingly to distances from $p_i$ (and uncertainty), what allows to also handle problematic saddles. Calculated gradients can be used to slowly evolve this parametrization to improve agreement with local behavior of objective function, accumulating their statistical trends: 1) update $\lambda, p$ parameters for more accurate description of parabola in corresponding directions (also uncertainty), 2) rotate considered subspace toward recently statistically significant directions (replacing the less frequent ones), and 3) rotate $(v_i)$ inside the subspace to improve diagonal form of Hessian in this basis. Presented general framework leaves many customization options for optimizations to specific tasks.

Last week, Kenneth Hayne QC handed down his royal commission final report that recommended banning banks from paying trail commissions to mortgage brokers from mid-next year. Instead, the borrower will likely be required to pay an upfront fee for the service. UBank, a subsidiary of NAB, doesn't pay mortgage brokers, but its new robot-like home loan aid gives a glimpse into how the service could be provided in the future. Many commentators are speculating only the wealthy will be able to afford a broker, while regular Aussies will have to rely on an automated service. The artificial loan aid, named Mia (My Interactive Assistant) and powered by AI start-up FaceMe, will speak directly to customers through a desktop or smartphone advising on questions such as what's a variable rate to what classifies as an expense, the bank says.

SABRA LANE: Much of the traditional car making industry around the world could go bust during the next decade, as the cost of electric cars plunge and become affordable for average motorists. That's the prediction of a top researcher and investor who thinks a world of self-driving electric cars, including autonomous taxis, is only a few years away. Brett Winton, the director of research at the US based Ark Invest, believes a disruptive day of reckoning is coming for complacent car making giants, who've failed to adapt their manufacturing models to confront new challengers like Tesla. Mr Winton is visiting Australia and he spoke about a not too distant "new world" of artificial intelligence with Senior Business Correspondent, Peter Ryan. You can look at some of the advances in artificial intelligence and see that computers are going to have the capability to solve the game of driving the car, and likely a lot safer than humans.