Deep learning with asymmetric connections and Hebbian updates

We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. To overcome the unrealistic symmetry in connections between layers, implicit in back-propagation, the feedback weights are separate from the feedforward weights. The feedback weights are also updated with a local rule, the same as the feedforward weights - a weight is updated solely based on the product of activity of the units it connects. With fixed feedback weights as proposed in Lillicrap et. al (2016) performance degrades quickly as the depth of the network increases. If the feedforward and feedback weights are initialized with the same values, as proposed in Zipser and Rumelhart (1990), they remain the same throughout training thus precisely implementing back-propagation. We show that even when the weights are initialized differently and at random, and the algorithm is no longer performing back-propagation, performance is comparable on challenging datasets. We also propose a cost function whose derivative can be represented as a local Hebbian update on the last layer. Convolutional layers are updated with tied weights across space, which is not biologically plausible. We show that similar performance is achieved with untied layers, also known as locally connected layers, corresponding to the connectivity implied by the convolutional layers, but where weights are untied and updated separately. In the linear case we show theoretically that the convergence of the error to zero is accelerated by the update of the feedback weights

When a Parent Is Incarcerated: A Primer for Social Workers

Offers guidance on meeting the needs of children of incarcerated parents, including reducing trauma, engaging incarcerated parents, re-entry planning, addressing domestic violence, and issues for parents in deportation proceedings. Includes resource list

The Impact of Immigration Enforcement on Child Welfare

While children of immigrants have a lot at stake in the discussions surrounding U.S. immigration policy, their interests remain largely ignored in the debate. For instance, little consideration is given to the impact of immigration enforcement on the 5.5 million children, the vast majority of whom are native-born U.S. citizens, living with at least one undocumented parent.Similarly overlooked are the significant challenges experienced by public child welfare agencies that encounter children separated from their parents due to immigration enforcement measures.The U.S. child welfare system is based on the notion of ensuring the safety and best interest of the child; however, this principle is often compromised in the face of conflicting federal immigration policies and practices. This policy brief examines the intersection of immigration enforcement and child welfare and the difficulties facing immigrant families caught between the two systems. Recommendations are provided to prioritize keeping children with their families and out of the public child welfare system whenever possible and to ensure that separated children who do encounter the child welfare system receive appropriate care and parents receive due process

Undercounted, Underserved: Immigrant and Refugee Families in the Child Welfare System

Focuses on the specific needs of immigrant and refugee children in the child welfare system and presents best practices and policy recommendations for better serving these populations

The Fallacy of Nuclear Primacy

"The United States is easily deterred by any nuclear armed state, even by the most primitive and diminutive of nuclear arsenals." Bruce G. Blair is the President of the World Security Institute. Chen Yali is the editor in chief of Washington Observer