FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across a wide class of prediction models, is to train a model ensemble. In a naive implementation, the ensemble approach has high computational cost and high memory demand. This challenges in particular modern deep learning, where even a single deep network is already demanding in terms of compute and memory, and has given rise to a number of attempts to emulate the model ensemble without actually instantiating separate ensemble members. We introduce FiLM-Ensemble, a deep, implicit ensemble method based on the concept of Feature-wise Linear Modulation (FiLM). That technique was originally developed for multi-task learning, with the aim of decoupling different tasks. We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison. Empirically, FiLM-Ensemble outperforms other implicit ensemble methods, and it and comes very close to the upper bound of an explicit ensemble of networks (sometimes even beating it), at a fraction of the memory cost.Comment: accepted at NeurIPS 202

HMC-Based Accelerator Design For Compressed Deep Neural Networks

Deep Neural Networks (DNNs) offer remarkable performance of classifications and regressions in many high dimensional problems and have been widely utilized in real-word cognitive applications. In DNN applications, high computational cost of DNNs greatly hinder their deployment in resource-constrained applications, real-time systems and edge computing platforms. Moreover, energy consumption and performance cost of moving data between memory hierarchy and computational units are higher than that of the computation itself. To overcome the memory bottleneck, data locality and temporal data reuse are improved in accelerator design. In an attempt to further improve data locality, memory manufacturers have invented 3D-stacked memory where multiple layers of memory arrays are stacked on top of each other. Inherited from the concept of Process-In-Memory (PIM), some 3D-stacked memory architectures also include a logic layer that can integrate general-purpose computational logic directly within main memory to take advantages of high internal bandwidth during computation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compression and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling controller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compres- sion and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling con- troller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation

Data-driven unsupervised clustering of online learner behaviour

The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional datasets: a different cohort of the same course, and time series of different format from another university

An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification

Data mining techniques have numerous applications in bankcard response modeling. Logistic regression has been used as the standard modeling tool in the financial industry because of its almost always desirable performance and its interpretability. In this paper, we propose a hybrid bankcard response model, which integrates decision tree-based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possible potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model is that adding variable interactions may improve the performance of logistic regression. Theoretically, all possible interactions could be added in logistic regression and significant interactions could be identified by feature selection procedures. However, even the stepwise selection is very time-consuming when the number of independent variables is large and tends to cause the p \u3e\u3e n problem. On the other hand, using CHAID analysis for the detection of variable interactions has the potential to overcome the above-mentioned drawbacks. To demonstrate the effectiveness of the proposed hybrid model, it is evaluated on a real credit customer response data set. As the results reveal, by identifying potential interactions among independent variables, the proposed hybrid approach outperforms the logistic regression without searching for interactions in terms of classification accuracy, the area under the receiver operating characteristic curve (ROC), and Kolmogorov-Smirnov (KS) statistics. Furthermore, CHAID analysis for interaction detection is much more computationally efficient than the stepwise search mentioned above and some identified interactions are shown to have statistically significant predictive power on the target variable. Last but not least, the customer profile created based on the CHAID tree provides a reasonable interpretation of the interactions, which is required by regulations of the credit industry. Hence, this study provides an alternative for handling bankcard classification tasks

Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel

Explaining generalizations and preventing over-confident predictions are central goals of studies on the loss landscape of neural networks. Flatness, defined as loss invariability on perturbations of a pre-trained solution, is widely accepted as a predictor of generalization in this context. However, the problem that flatness and generalization bounds can be changed arbitrarily according to the scale of a parameter was pointed out, and previous studies partially solved the problem with restrictions: Counter-intuitively, their generalization bounds were still variant for the function-preserving parameter scaling transformation or limited only to an impractical network structure. As a more fundamental solution, we propose new prior and posterior distributions invariant to scaling transformations by \textit{decomposing} the scale and connectivity of parameters, thereby allowing the resulting generalization bound to describe the generalizability of a broad class of networks with the more practical class of transformations such as weight decay with batch normalization. We also show that the above issue adversely affects the uncertainty calibration of Laplace approximation and propose a solution using our invariant posterior. We empirically demonstrate our posterior provides effective flatness and calibration measures with low complexity in such a practical parameter transformation case, supporting its practical effectiveness in line with our rationale

Scalable approximate inference methods for Bayesian deep learning

This thesis proposes multiple methods for approximate inference in deep Bayesian neural networks split across three parts. The first part develops a scalable Laplace approximation based on a block- diagonal Kronecker factored approximation of the Hessian. This approximation accounts for parameter correlations – overcoming the overly restrictive independence assumption of diagonal methods – while avoiding the quadratic scaling in the num- ber of parameters of the full Laplace approximation. The chapter further extends the method to online learning where datasets are observed one at a time. As the experiments demonstrate, modelling correlations between the parameters leads to improved performance over the diagonal approximation in uncertainty estimation and continual learning, in particular in the latter setting the improvements can be substantial. The second part explores two parameter-efficient approaches for variational inference in neural networks, one based on factorised binary distributions over the weights, one extending ideas from sparse Gaussian processes to neural network weight matrices. The former encounters similar underfitting issues as mean-field Gaussian approaches, which can be alleviated by a MAP-style method in a hierarchi- cal model. The latter, based on an extension of Matheron’s rule to matrix normal distributions, achieves comparable uncertainty estimation performance to ensembles with the accuracy of a deterministic network while using only 25% of the number of parameters of a single ResNet-50. The third part introduces TyXe, a probabilistic programming library built on top of Pyro to facilitate turning PyTorch neural networks into Bayesian ones. In contrast to existing frameworks, TyXe avoids introducing a layer abstraction, allowing it to support arbitrary architectures. This is demonstrated in a range of applications, from image classification with torchvision ResNets over node labelling with DGL graph neural networks to incorporating uncertainty into neural radiance fields with PyTorch3d

Fast and Accurate Inference on Microcontrollers with Boosted Cooperative Convolutional Neural Networks (BC-Net)

Arithmetic precision scaling is mandatory to deploy Convolutional Neural Networks (CNNs) on resource-constrained devices such as microcontrollers (MCUs), and quantization via fixed-point or binarization are the most adopted techniques today. Despite being born by the same concept of bit-width lowering, these two strategies differ substantially each other, and hence are often conceived and implemented separately. However, their joint integration is feasible and, if properly implemented, can bring to large savings and high processing efficiency. This work elaborates on this aspect introducing a boosted collaborative mechanism that pushes CNNs towards higher performance and more predictive capability. Referred as BC-Net, the proposed solution consists of a self-adaptive conditional scheme where a lightweight binary net and an 8-bit quantized net are trained to cooperate dynamically. Experiments conducted on four different CNN benchmarks deployed on off-the-shelf boards powered with the MCUs of the Cortex-M family by ARM show that BC-Nets outperform classical quantization and binarization when applied as separate techniques (up to 81.49% speed-up and up to 3.8% of accuracy improvement). The comparative analysis with a previously proposed cooperative method also demonstrates BC-Nets achieve substantial savings in terms of both performance (+19%) and accuracy (+3.45%)

Data envelopment analysis and data mining to efficiency estimation and evaluation

Purpose: This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance. Design/methodology/approach: Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression. Findings: The results showed that random forests and bagging outperform other methods in terms of predictive power. Originality/value: This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.Scopu