Robust Neural Pruning with Gradient Sampling Optimization for Residual Neural Networks

In this study, we explore an innovative approach for neural network optimization, focusing on the application of gradient sampling techniques, similar to those in StochGradAdam, during the pruning process. Our primary objective is to maintain high accuracy levels in pruned models, a critical challenge in resource-limited scenarios. Our extensive experiments reveal that models optimized with gradient sampling techniques are more effective at preserving accuracy during pruning compared to those using traditional optimization methods. This finding underscores the significance of gradient sampling in facilitating robust learning and enabling networks to retain crucial information even after substantial reduction in their complexity. We validate our approach across various datasets and neural architectures, demonstrating its broad applicability and effectiveness. The paper also delves into the theoretical aspects, explaining how gradient sampling techniques contribute to the robustness of models during pruning. Our results suggest a promising direction for creating efficient neural networks that do not compromise on accuracy, even in environments with constrained computational resources.

Continuous 16-bit Training: Accelerating 32-bit Pre-Trained Neural Networks

In the field of deep learning, the prevalence of models initially trained with 32-bit precision is a testament to its robustness and accuracy. However, the continuous evolution of these models often demands further training, which can be resource-intensive. This study introduces a novel approach where we continue the training of these pre-existing 32-bit models using 16-bit precision. This technique not only caters to the need for efficiency in computational resources but also significantly improves the speed of additional training phases. By adopting 16-bit precision for ongoing training, we are able to substantially decrease memory requirements and computational burden, thereby accelerating the training process in a resource-limited setting. Our experiments show that this method maintains the high standards of accuracy set by the original 32-bit training while providing a much-needed boost in training speed. This approach is especially pertinent in today's context, where most models are initially trained in 32-bit and require periodic updates and refinements. The findings from our research suggest that this strategy of 16-bit continuation training can be a key solution for sustainable and efficient deep learning, offering a practical way to enhance pre-trained models rapidly and in a resource-conscious manner.

StochGradAdam: Accelerating Neural Networks Training with Stochastic Gradient Sampling

In the rapidly advancing domain of deep learning optimization, this paper unveils the StochGradAdam optimizer, a novel adaptation of the well-regarded Adam algorithm. Central to StochGradAdam is its gradient sampling technique. This method not only ensures stable convergence but also leverages the advantages of selective gradient consideration, fostering robust training by potentially mitigating the effects of noisy or outlier data and enhancing the exploration of the loss landscape for more dependable convergence. In both image classification and segmentation tasks, StochGradAdam has demonstrated superior performance compared to the traditional Adam optimizer. By judiciously sampling a subset of gradients at each iteration, the optimizer is optimized for managing intricate models. The paper provides a comprehensive exploration of StochGradAdam's methodology, from its mathematical foundations to bias correction strategies, heralding a promising advancement in deep learning training techniques.

Linear Oscillation: The Aesthetics of Confusion for Vision Transformer

Activation functions are the linchpins of deep learning, profoundly influencing both the representational capacity and training dynamics of neural networks. They shape not only the nature of representations but also optimize convergence rates and enhance generalization potential. Appreciating this critical role, we present the Linear Oscillation (LoC) activation function, defined as $f(x) = x \times \sin(\alpha x + \beta)$. Distinct from conventional activation functions which primarily introduce non-linearity, LoC seamlessly blends linear trajectories with oscillatory deviations. The nomenclature ``Linear Oscillation'' is a nod to its unique attribute of infusing linear activations with harmonious oscillations, capturing the essence of the 'Importance of Confusion'. This concept of ``controlled confusion'' within network activations is posited to foster more robust learning, particularly in contexts that necessitate discerning subtle patterns. Our empirical studies reveal that, when integrated into diverse neural architectures, the LoC activation function consistently outperforms established counterparts like ReLU and Sigmoid. The stellar performance exhibited by the avant-garde Vision Transformer model using LoC further validates its efficacy. This study illuminates the remarkable benefits of the LoC over other prominent activation functions. It champions the notion that intermittently introducing deliberate complexity or ``confusion'' during training can spur more profound and nuanced learning. This accentuates the pivotal role of judiciously selected activation functions in shaping the future of neural network training.

An Efficient Approach to Mitigate Numerical Instability in Backpropagation for 16-bit Neural Network Training

In this research, we delve into the intricacies of the numerical instability observed in 16-bit computations of machine learning models, particularly when employing popular optimization algorithms such as RMSProp and Adam. This instability is commonly experienced during the training phase of deep neural networks, leading to disrupted learning processes and hindering the effective deployment of such models. We identify the single hyperparameter, epsilon, as the main culprit behind this numerical instability. An in-depth exploration of the role of epsilon in these optimizers within 16-bit computations reveals that a minor adjustment of its value can restore the functionality of RMSProp and Adam, consequently enabling the effective utilization of 16-bit neural networks. We propose a novel method to mitigate the identified numerical instability issues. This method capitalizes on the updates from the Adam optimizer and significantly improves the robustness of the learning process in 16-bit computations. This study contributes to better understanding of optimization in low-precision computations and provides an effective solution to a longstanding issue in training deep neural networks, opening new avenues for more efficient and stable model training.

G-NM: A Group of Numerical Time Series Prediction Models

In this study, we focus on the development and implementation of a comprehensive ensemble of numerical time series forecasting models, collectively referred to as the Group of Numerical Time Series Prediction Model (G-NM). This inclusive set comprises traditional models such as Autoregressive Integrated Moving Average (ARIMA), Holt-Winters' method, and Support Vector Regression (SVR), in addition to modern neural network models including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). G-NM is explicitly constructed to augment our predictive capabilities related to patterns and trends inherent in complex natural phenomena. By utilizing time series data relevant to these events, G-NM facilitates the prediction of such phenomena over extended periods. The primary objective of this research is to both advance our understanding of such occurrences and to significantly enhance the accuracy of our forecasts. G-NM encapsulates both linear and non-linear dependencies, seasonalities, and trends present in time series data. Each of these models contributes distinct strengths, from ARIMA's resilience in handling linear trends and seasonality, SVR's proficiency in capturing non-linear patterns, to LSTM's adaptability in modeling various components of time series data. Through the exploitation of the G-NM potential, we strive to advance the state-of-the-art in large-scale time series forecasting models. We anticipate that this research will represent a significant stepping stone in our ongoing endeavor to comprehend and forecast the complex events that constitute the natural world.

In Defense of Pure 16-bit Floating-Point Neural Networks

Reducing the number of bits needed to encode the weights and activations of neural networks is highly desirable as it speeds up their training and inference time while reducing memory consumption. For these reasons, research in this area has attracted significant attention toward developing neural networks that leverage lower-precision computing, such as mixed-precision training. Interestingly, none of the existing approaches has investigated pure 16-bit floating-point settings. In this paper, we shed light on the overlooked efficiency of pure 16-bit floating-point neural networks. As such, we provide a comprehensive theoretical analysis to investigate the factors contributing to the differences observed between 16-bit and 32-bit models. We formalize the concepts of floating-point error and tolerance, enabling us to quantitatively explain the conditions under which a 16-bit model can closely approximate the results of its 32-bit counterpart. This theoretical exploration offers perspective that is distinct from the literature which attributes the success of low-precision neural networks to its regularization effect. This in-depth analysis is supported by an extensive series of experiments. Our findings demonstrate that pure 16-bit floating-point neural networks can achieve similar or even better performance than their mixed-precision and 32-bit counterparts. We believe the results presented in this paper will have significant implications for machine learning practitioners, offering an opportunity to reconsider using pure 16-bit networks in various applications.