The paper argues that a convolutional sequence labeler’s receptive field is not the sole source of context. When a normalization layer computes statistics across the entire sequence at inference, it creates a sequence‑spanning path that supplies global context, effectively replacing the need for a larger receptive field. Experiments on synthetic labeling tasks, simulated genomes, and real 1000 Genomes haplotypes show that this global summary can match or exceed the performance of larger receptive fields, and that ablating receptive‑field‑enlarging blocks overestimates their importance.
By Qing Tian
The study investigates whether sensory-aligned receptive fields provide computational benefits beyond mere resource efficiency in recurrent networks of Expressive Leaky Memory neurons. Across auditory and event-based visual classification tasks, receptive fields aligned with task-relevant sensory coordinates improve test accuracy compared to budget-matched random fields, but this advantage disappears when coordinates are scrambled or irrelevant. The benefit diminishes as neuronal expressivity increases, and generic synaptic sparsity regularization only partially recovers performance, indicating that structured receptive fields act as a computational prior beyond sparsity alone.
By Agnese Adorante, Aaron Spieler, Anna Levina
arXiv:2607. 18759v1 Announce Type: new Abstract: Transformers with relative positional encodings often extrapolate to sequences longer than those seen during training, whereas transformers with learned absolute encodings typically do not.
By Subham Singh, Ashutosh Mishra, Subha Raut
The paper compares five machine unlearning (MU) methods—NegGrad, Fine‑Tuning (FT), Random Labeling (RL), SalUn, and MUNBa—on noisy‑label correction across CIFAR‑10, CIFAR‑100, and Food‑101N. Results show that the best MU strategy depends on the noise type: FT works well for most closed‑set noise, RL and SalUn are robust and nearly match retraining accuracy under instance‑dependent noise, while MUNBa excels only under extreme symmetric noise. In open‑set noise, retraining on the cleaned data actually hurts performance, indicating that approximating retraining is not suitable in that regime, yet all MU methods still achieve near‑retraining accuracy on Food‑101N with much lower runtime.
By Jo\~ao L. P. Santana, Filipe R. Cordeiro
arXiv:2608.30720v1 Announce Type: new
Abstract: Representational similarity is foundational to analyses of deep networks, yet distances between point-valued representations are not intrinsically tied...
By Kieran Murphy
The paper presents a mean‑field analysis of attention in language models, defining an average attention kernel that propagates representations layer by layer. When conditioned on a whole corpus, the kernel predicts the average evolution of representation geometry; when conditioned on a single context, it predicts the expected geometry for that context. The difference between actual attention and the mean‑field prediction—called the mean‑field deviation—captures context‑specific computation, revealing how models diverge from average behavior during training and in few‑shot tasks.
By Micah Adler, John W. Byers, Mark Crovella