000			01648nam a22001697a 4500
082			_a006.31 _bM476D
100			_aMazumder, Sudipta _945441
245			_aDesigning Optimizer for Deep Learning _cby Sudipta Mazumder
260			_aIIT Jodhpur _bDepartment of Computer Science and Technology _c2023
300			_avii,10p. _bHB
500			_aOptimizers form the backbone of any convolutional network as they are responsible for making the functions converge faster. The optimizers do this by modifying the weights and learning rate of the algorithm, which reduces the loss and improves the accuracy. A lot of optimizers have gained traction over the years, out of which SGD and Adam take the cake. Adam has taken the lead as it helps to reduce the dying gradient problem of SGD. However, we still have scope for improvement. With this paper, we aim to introduce a new algorithm that surpasses the performance of Adam by calculating the angular gradients (cosine and tangent angles) at consecutive steps. This algorithm uses the gradient of the current step, the previous step, and the step previous to that. As we present more information to the optimizer’s algorithm, the algorithm has more information, making it better poised to make more accurate predictions at faster convergence rates. We have tested this approach on benchmark datasets and compared it with other state-of-the-art optimizers, and have obtained superior results in almost every approach.
650			_aDepartment of Computer Science and Technology _945442
650			_aOptimizers _945443
650			_aMTech Theses _945444
700			_aPaul, Angshuman _945445
942			_cTH
999			_c16588 _d16588