Making a speech recognition model insensitive to spatial variations of the pattern

Making a speech recognition model insensitive to temporal variations of the pattern

Making a audio-visual speech generation model

Making a audio-visual speech generation model which can’t deal with lots of noise

Series of binary searches in weight space

Series of linear searches in parameter space

Series of linear searches in parameter and weight space

Series of binary searches in parameter and weight space

Inputs

Features values

Auxiliary inputs

Labels

Convolving kernel passes through the image in a sliding-window manner

Convolving kernel can’t replicate in further layers

Convolving kernels are same across different layers of the CNN

Convolving kernel weights become independent of the position of training pattern

Hessian Matrix

AdaDelta and Stochastic Gradient Descent

AdaGrad and L-BFGS

Line search

Sorting the test samples according to difficulty and training using mini-batches

Grading the training examples according to difficulty and proving a series of mini-batches which increasingly become difficult

Grading the training examples according to difficulty and proving a series of mini-batches which increasingly become difficult and testing using sorted data

Grading the training examples according to difficulty and proving a series of mini-batches which increasingly become easy

Higher variance, lower bias, a penalty term to maximize it

Lower variance, higher bias and a penalty term to maximize it

Lower variance, higher bias and greater size of testing data

Higher variance ,lower bias and developing a satisficing model

Makes the trainable parameters to be learnt

Makes the non-trainable parameters to be copied with modifications according to the end-part of the model

Maximizes the amount of learning by using the weights of pre-trained model

Minimizes the amount of learning by using the weights of pre-trained model

Type of solution to a kind credit assignment problem

Forward propagation and prediction

Forward propagation and classification

Training and testing of the model

X and Y

X

Y or X

X but not Y

tf.debugging.set_log_device_placement(True)

physical_devices = tf.config.list_physical_devices('GPU')

tf.config.experimental.set_memory_growth(physical_devices[0, True)

tf.debugging.set_log_device_placement(True)

physical_devices = tf.config.list_physical_devices('GPU/0')

tf.config.experimental.set_memory_growth(physical_devices[1], True)

tf.debugging.set_log_device_placement(True)

tf.config.experimental.set_memory_growth(physical_devices[0], False)

physical_devices = tf.config.list_physical_devices('GPU')

tf.config.experimental.set_memory_growth(physical_devices[1], True)