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Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska · Benjamin Cowen · Sadhana Kumaravel · Ronny Luss · Mattia Rigotti · Irina Rish · Paolo DiAchille · Viatcheslav Gurev · Brian Kingsbury · Ravi Tejwani · Djallel Bouneffouf

Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Hall B

Despite significant recent advances in deep neural networks, training them remains a challenge due to highly non-convex nature of the objective function. State-of-art methods rely on error backpropagation, which suffers from several well-known issues, such as vanishing and exploding gradients, inability to handle non-differentiable nonlinearities and to parallelize weight-update across layers, and biological implausibility. These limitations continue to motivate exploration of alternative training algorithms, including several recently proposed auxiliary-variable methods which break the complex nested objective function into local subproblems, avoiding gradient chains and thus the vanishing gradient issue, allowing weight update parallelization, among other advantages. However, those techniques are mainly offline (batch), which limits their applicability to extremely large datasets or unlimited data streams in online, continual or reinforcement learning. The main contribution of our work is a novel online (stochastic/mini-batch) alternating minimization (AM) algorithm for training deep neural networks, together with the first theoretical convergence guarantees for AM in stochastic settings, and extensive empirical evaluation on various architectures and datasets, demonstrating advantages of the proposed approach as compared to both offline auxiliary variable methods and to the backpropagation-based stochastic gradient descent.

Author Information

Anna Choromanska (New York University)
Benjamin Cowen (NYU)
Sadhana Kumaravel (IBM Research)
Ronny Luss (IBM Research)
Mattia Rigotti (IBM Research AI)
Irina Rish (IBM Research AI)
Paolo DiAchille (IBM Research)
Viatcheslav Gurev (IBM Research)
Brian Kingsbury (IBM Research)
Ravi Tejwani (MIT)
Djallel Bouneffouf (IBM Research)

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